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Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are among the most revolutionary breakthroughs by the context of decarbonization of road transport. The adoption of EVs has drawn increasing attention and is currently considered a potential route towards sustainable transportation. Businesses approaching the EV market must establish lucrative business models that overcome the adoption obstacles for EVs while assuring long-term growth. The intent of the current research exploration is to create a cutting-edge framework for EV enterprises that aims to maximize profit and save delivery time leading to high customer satisfaction. The volume discount strategy is used for profit escalation. The model presented in this paper is highly adaptable for a real-world problem as it incorporates uncertainty in the EV industry through generalized triangular neutrosophic numbers, an extension of fuzzy numbers. A modified version of the neutrosophic compromise programming approach is proposed in this paper. The results from this approach are validated using genetic algorithm. The proposed optimization-based framework is encapsulated in the form of study of a case from Bangalore city, in India. An in-depth exploration of two cases along with sensitivity analysis is carried out that helped in prioritizing some discrete customers and gave insights on which manufacturing site should be designated more focus. • Profit maximizing business model for electric vehicle (EV) merchants is proposed. • Uncertainty in EV industry is captured using fuzzy number. • A modified neutrosophic compromise programming technique is proposed. • Case study is considered involving particular case and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%. • This paper solves the radar placement problem using a set of metaheuristic approaches. • The problem is considered as a multi-objective optimization problem. • Two adaptive and enhanced versions of the NSGA-II algorithm are proposed. • The results show that a coverage rate of 98% can be achieved with just a few radars. [ABSTRACT FROM AUTHOR]

• Hub capacities are considered for the single-allocation Hub Maximal Covering problem. • A new Mixed-Integer Linear-Programming model is proposed and solved for the problem. • A Comprehensive fitness landscape analysis shows the problem space is rugged plain. • A new GARVND algorithm with original Crossover and Mutation operators is proposed. • Experiments showed that GARVND outdid GA, SA, VNS, TS, & GWO+SA methods in objective value. In this paper, a new variation to the Hub Maximal Covering problem called the Capacitated Single-Allocation Hub Maximal Covering Problem (CSAHMCP) is addressed for the first time and a novel binary-integer mathematical model has been proposed. To analyze the features of the problem's solution space and to determine the most effective heuristic approach to solve it, a comprehensive Fitness Landscape Analysis is performed which reveals that the problem is of 'rugged plain' type and can best be solved using an algorithm with good exploration and exploitation capabilities at the same time. Accordingly, we proposed a new algorithm named GARVND, which is a hybrid of the Genetic Algorithm acting as the main framework for diversification, and a variant of the Variable Neighborhood Descent algorithm called 'Reduced VND' acting as a means to improve individual solutions (intensification) by searching in the spaces generated by four types of neighborhood operators. We also introduced two original crossover and mutation operators that can be used for any hub location solution method. The parameters of GARVND are tuned toward less computational times and higher solution quality by using the Taguchi experiment design technique. We solved 114 CSAHMCP instances (several times each) from the Australian Post (AP) dataset using the proposed GARVND, as well as by the Genetic Algorithm, Variable Neighborhood Search, Simulated Annealing, Tabu Search algorithms, and a hybrid algorithm consisting of Grey Wolf Optimizer and Simulated Annealing, in addition to the Gurobi exact solver. Extensive computational results and statistical analyses showed that GARVND yields favorable solutions, outperforming all the compared algorithms by producing smaller gaps with global optimal objective values. In addition, to illustrate the practical value of the CSAHMCP and the GARVND method, we performed a cost-flow sensitivity analysis for the AP Central Sydney network and came up with the best expansion scenarios to be adopted by the management. [ABSTRACT FROM AUTHOR]

Urban drainage system plays an important role in urban rainwater management. The design of urban drainage system is challenging for its complex constraints and expensive individual evaluation. A kind of multi-objective evolutionary algorithm driven by both structural and empirical knowledge, called structural and empirical knowledge driven multi-objective evolutionary algorithm, is proposed in this paper. A novel tree-template-based encoding schema is proposed to extract the structural knowledge of urban drainage system. The population initialization method and genetic operators (i.e., crossover and mutation) are customized to handle the constraints among decision variables caused by the structure restriction of the urban drainage system. The radial basis function (RBF) model is employed to learn the empirical knowledge from the previous evolution process, and the RBF-based approximate evaluation is used to partially replace the expensive individual evaluation to improve the efficiency of the algorithm. Besides, by combining the decomposition idea and the constrained dominance principle, the local constrained dominance principle is suggested to handle the constraints in decision space, which can effectively improve the defect that the constrained dominance principle is easy to fall into local optimum. Comprehensive experiments on two urban drainage system cases show the superiority of the proposed algorithm for the optimal urban drainage system design. [ABSTRACT FROM AUTHOR]

Maintenance planning and scheduling are an essential part of manufacturing companies to prevent machine breakdowns and increase machine uptime, along with production efficiency. One of the biggest challenges is to effectively address uncertainty (e.g., unexpected machine failures, variable time to repair). Multiple approaches have been used to solve the maintenance scheduling problem, including dispatching rules (DR), metaheuristics and simheuristics, or most recently reinforcement learning (RL). However, to the best of our knowledge, no study has ever studied to what extent these techniques are effective when faced with different levels of uncertainty. To overcome this gap in research, this paper presents an approach by analyzing the impact of categorized levels of uncertainty, specifically high and low, on the failure distribution and time to repair. Upon the formalization of the maintenance scheduling problem, the experiments conducted are performed in simulated scenarios with different degrees of uncertainty, and also considering a real-life manufacturing use case. The results indicate that rescheduling based on a genetic algorithm (GA) simheuristic outperforms RL and DR in terms of total machine uptime, but not in terms of the mean time to repair when configured with high re-optimization frequencies (i.e., hourly re-optimization), but rapidly underperforms when the re-optimization frequency decreases. Furthermore, our study demonstrates that GA-simheuristic is highly computationally demanding compared to RL and rule-based policies. • Problem of dynamic maintenance scheduling under uncertainty is assessed. • Reinforcement Learning, Genetic Algorithms and Dispatching rules were compared. • Exploration of uncertainty factors such as time to repair and failure distribution. • Artificial and real-based scenarios were evaluated. [ABSTRACT FROM AUTHOR]

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG-HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison. • We propose using spectral clustering to group the features so that the in-group feature similarity is extremely high. • We propose a redundant feature filter algorithm since existing algorithms cannot filter redundant features. • we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets. [ABSTRACT FROM AUTHOR]

In recent years, the application of multi-UAV cooperation systems has expanded across various domains. Enhancing the coordination performance of multi-UAV systems can be achieved through task allocation methods, typically relying on a hierarchical structure. This paper proposes a novel approach using a modified genetic algorithm (GA) to address the integrated task allocation and path planning problems for multi-UAV attacking multi-target. To create a more realistic mission scenario, multiple constraints, such as resource requirement and simultaneous target arrival, are considered. The modified GA incorporates tailored crossover and mutation operators that ensure compliance with the aforementioned constraints. Furthermore, an unlocking strategy is devised to prevent the occurrence of a chromosome deadlock condition, in which several UAVs become stuck in an infinite waiting state. Through simulation results, the modified GA is demonstrated to effectively delivers feasible solutions to the coupled task allocation and path planning problems, preserving the integrated nature of the optimization process. Monte Carlo simulations are conducted to highlight the superiority of the proposed method in comparison to conventional approaches. [ABSTRACT FROM AUTHOR]

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear. [ABSTRACT FROM AUTHOR]

The class imbalance problem is one of the main challenges that hinders classifiers from identifying unknown instances. When class distribution imbalance and class overlap coexist in the same data set, the recognition of minority instances by traditional classifiers will be seriously affected. In the binary classification problem, the relationship between the majority class and the minority class is clear, and the overlap problem between classes can be well solved by removing the majority instances in the class-overlap area. However, for multi-class imbalance problems, the relationships between classes will become complex. Clustering techniques are effective methods for identifying overlapping instances; however, a single clustering method cannot effectively identify clusters of different shapes. Therefore, in this paper, a H eterogeneous C lustering E nsemble learning method for M ultiple C lass-overlap D etection (HCE-MCD) is proposed for multi-class imbalance problems. This method uses a genetic algorithm to select a combination of heterogeneous clustering techniques with the best fitness function from a pool of clustering algorithms and uses majority voting to fuse the results of the clustering algorithm. Furthermore, instances located near the decision boundary are more likely to be overlapping instances. Therefore, according to the clustering results of the heterogeneous clustering ensemble method, we can identify the overlapping instances existing in the data set, thereby eliminating the class overlap problem in the multi-class dataset. Experimental results on 19 open-access datasets show that our proposed method outperforms or partially outperforms state-of-the-art research protocols. [ABSTRACT FROM AUTHOR]

• We have developed a clustering algorithm for an unknown number of clusters by MOGA. • It works with continuous and categorical featured data sets. • It can work with data sets having missing values. • The final solution is selected by majority vote by all non-dominated solutions. • Context-sensitive and cluster-orient genetic operators are designed. Many clustering algorithms categorized as K -clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K -clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K -Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GA s) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency. [ABSTRACT FROM AUTHOR]

When the evolutionary algorithm is applied to handle high-dimensional expensive multi-objective optimization problems (MOPs), population evolution is crucial since it controls exploration and exploitation and decides if promising candidate solutions could be generated. However, little attention has been paid to this issue, evolution operators based on Genetic Algorithm (GA) and Differential Evolution (DE) are still the two most common approaches, whose convergence on high-dimensional MOPs with a limited number of fitness evaluations remains challenging. In this paper, we propose a decomposition-based local learning strategy to accelerate convergence in the high-dimensional search space of MOPs. Specifically, an individual is updated by learning from one of the best solutions of its corresponding local area based on the multi-objective decomposition approach. Accordingly, a surrogate-assisted evolutionary algorithm is proposed for better solving expensive high-dimensional MOPs. Experimental studies on MOPs with up to 100 decision variables and with 300 fitness evaluations demonstrate the effectiveness of the proposed method. Furthermore, we use the proposed method to solve a 2-objective shape design problem of the blended wing-body underwater glider (BWBUG) with 39 decision variables, and an impressive solution set is obtained. • Efficacy of evolution operators on high-dimensional EMOPs is analyzed. • Propose a novel evolution strategy based on decomposition for high-dimensional EMOPs. • A surrogate-assisted evolutionary algorithm is accordingly developed. • Comprehensive experimental studies on benchmark problems and an engineering application. [ABSTRACT FROM AUTHOR]

Over the years, the study on logic mining approach has increased exponentially. However, most logic mining models disregarded any efforts in expanding the search space which led to poor generalizability property of the retrieved induced logic. In light of this gap, this paper initiated the hybridization of logic mining approach with a multi-objective training algorithm namely Modified Niche Genetic Algorithm. The core impetus of this algorithm is to ensure optimal production of multiple superstrings via Wan Abdullah method resulting in multiple units associative memory feature of the Discrete Hopfield Neural Network. Therefore, the storage capacity of DHNN increases which directed towards larger search space of locating optimal induced logic. Additionally, several modifications were imposed to counter other issues such as, rigid logical rule, outdated quality of best logic, and high dependency on the supervised attributes selection method. Experimentation was done on 20 repository datasets from reputable machine learning repositories. Results showed that the proposed model outperformed all baseline methods in terms of accuracy = 0.8727, precision = 0.9845, specificity = 0.9988, and Matthew's correlation coefficient = 0.5815. [ABSTRACT FROM AUTHOR]

• A novel algorithm was introduced for path planning in continuous environment. • A deterministic algorithm was introduced to determine all feasible initial paths. • A Genetic Algorithm with five new crossover and mutation operators was developed. • Path length, smoothness, and safety were combined to form the objective function. • The proposed algorithm was extended to handle the multi-robot problem. Abstract This paper presents a hybrid approach for path planning of multiple mobile robots in continuous environments. For this purpose, first, an innovative Artificial Potential Field (APF) algorithm is presented to find all feasible paths between the start and destination locations in a discrete gridded environment. Next, an enhanced Genetic Algorithm (EGA) is developed to improve the initial paths in continuous space and find the optimal path between start and destination locations. The proposed APF works based on a time-efficient deterministic scheme to find a set of feasible initial paths and is guaranteed to find a feasible path if one exists. The EGA utilizes five customized crossover and mutation operators to improve the initial paths. In this paper, path length, smoothness, and safety are combined to form a multi-objective path planning problem. In addition, the proposed method is extended to deal with multiple mobile robot path planning problem. For this purpose, a new term is added to the objective function which measures the distance between robots and a collision removal operator is added to the EGA to remove possible collision between paths. To assess the efficiency of the proposed algorithm, 12 planar environments with different sizes and complexities were examined. Evaluations showed that the control parameters of the proposed algorithm do not affect the performance of the EGA considerably. Moreover, a comparative study has been made between the proposed algorithm, A*, PRM, B-RRT and Particle Swarm Optimization (PSO). The comparative study showed that the proposed algorithm outperforms PSO as well as well-recognized deterministic (A*) and probabilistic (PRM and B-RRT) path planning algorithms in terms of path length, run time, and success rate. Finally, simulations proved the efficiency of the proposed algorithm for a four-robot path planning problem. In this case, not only the proposed algorithm determined collision-free paths, but also it found near optimal solution for all robots. [ABSTRACT FROM AUTHOR]

Highlights • The criteria for the selection of green suppliers are of vital importance. • Model of Green Procurement with Coloured Petri Nets is proposed in a generic way. • Flexibility, graphical structure and synthesis of the PN model. • Future research will use fuzzy logic to apply them in the selection of suppliers. Abstract In this paper, an extension and an application to the green procurement model based on Coloured Petri Nets is presented. The large number of services and providers for services that can exist in a company means a problem when it comes to managing purchases and much more if the purchases. If the purchases have a benefit such as the sustainability of the companies processes that provide the service, the problem is even greater. This paper aims to develop an extension for this proposed model using the petri nets tools thanks to its ease of modeling complex systems. Likewise, a new algorithm applied to the place of selection of suppliers and a methodical mathematical formalization was developed. Finally, two examples are implemented. The first example allows understanding and applying the model in a simple way. In the second example or study case, be proposed an application in a services company. This example describes the step by step of the evolution of the model and the results obtained by each stage. The application of the final example shows the qualitative advantages of the developed model and the need to apply some technique of quantitative analysis of easy integration to the model that will be defining in a future research. [ABSTRACT FROM AUTHOR]

Recent advancements in computer vision and augmented reality (AR) technology have created a pathway for enhanced human–computer interaction. Despite the potential benefits, the integration of these technologies is not yet common in the industry. Object detection is the foundation of AR systems for inspection, and an efficient object detection model will enable accurate object localization in AR devices. However, the limited computing capabilities and memory usage of wearable AR devices pose a significant challenge to the deployment of state-of-the-art detectors on these devices in real work scenarios. Moreover, as the person wearing the AR device keeps moving, the constantly changing scale of the captured image often leads to significant information loss when feature maps of different scales are fused. To address these challenges, this paper proposes a new compatible detector YOLO-Master based on improved YOLOv5 through deep learning and AR. The paper introduces several improvements, such as using MobileNetv3 as the backbone to reduce memory overhead and computational costs and designing an attention module MCA based on coordinate to reduce information loss. Besides, 20 hyperparameters of YOLOv5 are optimized by the genetic algorithm(GA) before model training, which can help reduces the loss value generated. YOLO-Master was trained and tested on hydropower device images collected from a power plant in the China Southern Power Grid, and the experiments on a self-made dataset demonstrated that the mAP of the proposed method can reach 70.1%, which is 2.9% higher than the baseline of 67.2%. Additionally, the number of parameters of the model was reduced by 66.7%, enabling the model to ensure accuracy while being compatible with real-time performance, meeting the urgent needs of engineering deployment. • A Compatible detector is proposed for device detection in AR inspection system. • Computation cost is improved by a lightweight backbone network. • A coordinate-based attention module is designed for effective feature fusion. • Optimizing hyperparameters of the new architecture to make the detector robust. [ABSTRACT FROM AUTHOR]

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta -models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta -model provides reliable estimation with an average RMSE and MAE of 2.604 m m / m i n and 3.402 m m / m i n for TBM's penetration rate(O 1), 0.0211 and 0.0324 for over/under excavation ratio (O 2), and 15.512 k w h and 23.088 k w h for energy consumption (O 3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM's performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O 1 to O 3 , respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O 1 to O 3 , respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation. [ABSTRACT FROM AUTHOR]

Effectively scheduling a project is crucial for its success, especially after generating work packages from the work breakdown structure during the planning phase. Nevertheless, solving project scheduling problems with multiple work packages is challenging due to the inefficient utilization of work package information in existing scheduling approaches. To address this issue, this paper proposes the Heuristic Rule Adaptive Selection (HAS) approach for the Multi-Work Package Project Scheduling Problem (MWPSP). This approach involves work package information and employs reinforcement learning (RL) for intelligent decision-making in scheduling. First, the MWPSP with the optimization objective of minimizing the Portfolio Delay (PDEL) and the Average Percent Delay (APD) is defined, and a scheduling environment is established that integrates information from both work packages and tasks. Second, a Double Deep Q-network (DDQN) is employed to train agents for adaptively selecting heuristic rules of tasks and work packages. The performance of the HAS approach is then evaluated using a case project and the newly created MWPSP dataset. The experimental results demonstrate that the HAS approach exhibits superior solution quality and computational efficiency in optimizing PDEL and APD compared to heuristics approaches, e.g., single-priority rule-based heuristics and genetic algorithms. This achievement sets the stage for the development of next-generation adaptive scheduling for construction projects. [ABSTRACT FROM AUTHOR]

The characters of large volume, complex dependency and frequent reuse of the data in data-intensive scientific workflow systems make the data management more and more complex but crucial. Automating the data management is an urgent need for scientific workflow systems and, requires accurate models and efficient methods for data storage and request response. This paper deals with the problems of data regeneration and storage optimization by using data dependency for cloud scientific workflow systems. Through representing the data storage strategies as 0-1 strings, a global discrete model for data storage optimization is constructed, which has a complexity of O (2 n) (n is the dataset number). In terms of data regeneration, we develop a deterministic method to determine optimal regeneration strategies for requested datasets with minimal computation under any data storage strategy, such that the systems can satisfy the data requests automatically and evaluate the computation cost accurately. To solve this storage optimization problem, the enumeration method and elitist canonical genetic algorithm are constructed by shortening the representation of storage strategies to avoid redundant computation. At last, experiments are conducted to test the proposed methods. Experimental results and comparisons show the positiveness and effectiveness of the proposed methods for data management in cloud scientific workflow systems. • Optimal deterministic data regeneration method using data dependency is proposed. • Data storage optimization models with solution algorithms are developed. • Experiments on complex nonlinear problems show the superiority of our approach. [ABSTRACT FROM AUTHOR]

A vision system comprises several steps, with each step exerting a significant impact on the final outcome. One of these crucial steps is segmentation, which isolates the region of interest within an object and removes the background. Segmentation is vital because it enhances the quality of the isolated region, improves the accuracy of extracted features, and reduces the noise introduced by poor-quality features. Several segmentation techniques are available in the literature, each requiring one or more adjustable parameters. Selecting the most appropriate technique for segmenting a particular image dataset can be challenging. Several factors can affect the segmentation quality, including preprocessing, the chosen segmentation method, parameter fine-tuning approaches, and the characteristics of the dataset. Moreover, variations in lighting and intensity can further influence segmentation quality. At times, an expert may need to manually choose the segmentation technique and fine-tune its associated parameters. This paper presents the development of an automated algorithm for the selection of segmentation techniques and their associated parameters. The developed techniques are implemented and compared using diverse datasets, and the resulting experimental outcomes are thoroughly discussed and analyzed. The algorithm aims to streamline and simplify the process of selecting appropriate segmentation techniques, determining the required parameters, and selecting suitable pre-processing techniques. • New method to segmentation of image data-sets. • The proposed algorithm prevents poor segmentation due to bright changes. • The proposed algorithm improves the quality of the data-set segmentation. • Automatically found best parameters and space color to segmentation. • We compare the results using PRI VOI and GCE metrics. [ABSTRACT FROM AUTHOR]

Compositional data (CoDa) is a type of relative data reflecting the proportion of different units in an integrated system, which can reveal the internal structure of observed objects and has been widely used to analyze practical economic and management problems. However, the summation constraint limits the prediction of compositional data time series. Especially, with small samples and poor information characteristics of the data set, the difficulties are further exacerbated. Compared with real-valued time series analysis, fuzzy time series (FTS) mainly transforms initial observed data to be different status so that the historical evolution rules can be derived. As a result, forms of initial observed data can be concealed. Correspondingly, this paper aims to establish a multi-rule combination prediction of compositional data based on a multivariate fuzzy time series model, named GA-CoDa-MFTS. Firstly, with the interaction of components, the adjustment rules between components are explored by using the fuzzy logical relationship (FLR). Secondly, the Genetic algorithm (GA) is used to find the optimal weight of the predicted values under different rules and derive the combined predicted values. Finally, the prediction effectiveness of the model is verified by CRMSE and CMAPE through a numerical study. • The fuzzy time series model and the compositional data are integrated. • The adjustment rules are explored by using the fuzzy logical relationship. • Genetic algorithm is used to get the optimal weight of different rules. • The combination model composed of different rules has better prediction effect. [ABSTRACT FROM AUTHOR]

In this paper, we study the personnel task rescheduling problem with time preemption. We assume a baseline task schedule has been composed some time in advance that has been rendered infeasible due to operational uncertainty. When restoring the schedule feasibility, the personnel planner takes several considerations into account encompassing the provided service level, resource stability, and task adherence. The latter stems from the possibility that in the problem under study tasks can be interrupted and resumed later, better known as time preemption. We propose a genetic algorithm complemented with local search techniques to compose an operational schedule in an effective manner. The genetic algorithm operates on a dedicated decoding mechanism to compose high-quality duties for individual workers efficiently. Computational experiments are conducted to benchmark the performance of the proposed procedure with other exact and (meta-)heuristic solution procedures, and to demonstrate the value of the algorithm design choices. Furthermore, the impact of preempting tasks in time is assessed by changing the objective function structure and the proportion of non-preemptive tasks. • Introduction of time preemption as recovery action for personnel task rescheduling. • Dedicated genetic algorithm combined with local search techniques. • Effective encoding–decoding mechanism to compose individual work duties efficiently. • Time preemption and associated retardation of tasks facilitates schedule recovery. • Proposed method and components outperform alternative exact and heuristic methods. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has a network of interconnected medical devices to capture patients' health metrics and store them in a centralized server for analysis by medical experts. The security concerns in IoMT data are therefore very high. The attackers may inject malware into the IoMT data during its transmission. If the health parameters are affected by malware it may mislead the medical experts in making inferences about the patient's health. IoMT devices are resource-constrained and require faster analysis of data for better medical assistance. Most of the classification techniques used earlier suffer from exhaustive time and resource consumption. Hence, developing an intelligent framework that could reduce the data size and classify the malware quickly is essential. This paper proposes a deep learning framework called Auto-encoder to encode the IoMT data. The encoded features are then given to an XGBoost Classifier whose hyperparameters are optimized using the Genetic Algorithm. XGBoost Classifier detects the presence of malware in the IoMT dataset and Clamp dataset with an accuracy of 98.98% and 98.69% respectively. This lightweight model achieves dimensionality reduction through Autoencoder and effectively detects malware through an optimized XGBoost classifier with faster convergence and limited computational costs. Implementing this framework on the fog layer provides promising results by optimizing resource usage and saving time compared to previous works. Moreover, the proposed method does not need explicit feature selection from the dataset when making predictions. [ABSTRACT FROM AUTHOR]

Due to the importance of water resources management that requires the optimal model for streamflow prediction, the study of water flow prediction is of great importance. Hence, the present paper aims to introduce an innovative model that can predict streamflow with the highest accuracy. Accordingly, a multilayer perceptron neural network whose optimum neuron number is specified using the evaluation metrics is considered for simulation. The dataset used for this purpose consists of 80% experimental and 20% numerical data. Accordingly, the MLP network used in this research is optimized using various optimizers, namely Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Chaotic Grey Wolf Optimization, Advanced Grey Wolf Optimization, and Adaptive Chaotic Grey Wolf Optimization. Considering the statistical results, Adaptive Chaotic Grey Wolf Optimization is introduced as the leading optimizer. The obtained results highlight that Adaptive Chaotic Grey Wolf Optimization used for optimizing the training process of the MLP network with 15 neurons can predict the daily streamflow with the best standard error, mean square error, mean absolute percent error, mean absolute error, and normalized mean square error of about 5.3126, 2.3049, 0.6684, 1.1038, and 0.4483, respectively. [ABSTRACT FROM AUTHOR]

The accumulation of large amounts of historical data has led to the wide application of ensemble learning over the past few decades, but the balance between the individual accuracy of base classifiers (BCs) and the diversity among these BCs is rarely considered in the construction of ensemble models. Since such a balance is crucial to the success of ensemble models, this paper proposes a Genetic Algorithm-based sequential instance selection framework to address this research gap. The novelties of the proposed framework include: transforming the balance between the individual accuracy of BCs and the diversity among BCs into a general combinatorial optimization model and designing a Genetic Algorithm-based evolutionary instance selection method to solve this model. The proposed framework not only overcomes the inherent limitations of the Genetic Algorithm in some high-dimensional tasks but also provides an explicit and automatic way to balance the accuracy and diversity by searching appropriate training data subsets for different component BCs. Based on obtained training data subsets, the component BCs of the ensemble model are generated sequentially, and their predictions are further combined with the weighted majority voting rule. Using 30 real datasets collected from various practical applications, such as medicine, business, and industry, the effectiveness of the proposed framework in constructing powerful ensemble models is examined and compared with six benchmark ensemble learning methods. In addition, the capability of the proposed framework to improve convergence performances is also examined by the comparison with the traditional Genetic Algorithm. [ABSTRACT FROM AUTHOR]

With the penetration of Internet of things in manufacturing industry, it is an unavoidable issue to maintain robust wireless connections among machines and human workers in harsh industrial environments. However, the existing wireless planning tools focus on office environments, which are less harsh than industrial environments regarding shadowing effects of diverse obstacles. To fill this gap, this paper proposes an over-dimensioning (OD) model, which automates the decision making on deploying a robust industrial wireless local area network (IWLAN). This model creates two full coverage layers while minimizing the deployment cost, and guaranteeing a minimal separation distance between two access points (APs) to prevent APs that cover the same region from being simultaneously shadowed by an obstacle. Moreover, an empirical one-slope path loss model, which considers three-dimensional obstacle shadowing effects, is proposed for simple yet precise coverage calculation. To solve this OD model even at a large size, an efficient genetic algorithm based over-dimensioning (GAOD) algorithm is designed. Genetic operators, parallelism, and speedup measures are tailored to enable large-scale optimization. A greedy heuristic based over-dimensioning (GHOD) algorithm is further proposed, as a state-of-the-art heuristic benchmark algorithm. In small- and large-size OD problems based on industrial data, the GAOD was demonstrated to be 20%–25% more economical than benchmark algorithms for OD in the same environment. The effectiveness of GAOD was further experimentally validated with a real deployment system. Though this paper focuses on an IWLAN, the proposed GAOD can serve as a decision making tool for deploying other types of robust industrial wireless networks in terms of coverage, such as wireless sensor networks and radio-frequency identification (RFID) networks. [ABSTRACT FROM AUTHOR]

This paper proposes three feature selection algorithms with feature weight scheme and dynamic dimension reduction for the text document clustering problem. Text document clustering is a new trend in text mining; in this process, text documents are separated into several coherent clusters according to carefully selected informative features by using proper evaluation function, which usually depends on term frequency. Informative features in each document are selected using feature selection methods. Genetic algorithm (GA), harmony search (HS) algorithm, and particle swarm optimization (PSO) algorithm are the most successful feature selection methods established using a novel weighting scheme, namely, length feature weight (LFW), which depends on term frequency and appearance of features in other documents. A new dynamic dimension reduction (DDR) method is also provided to reduce the number of features used in clustering and thus improve the performance of the algorithms. Finally, k-mean, which is a popular clustering method, is used to cluster the set of text documents based on the terms (or features) obtained by dynamic reduction. Seven text mining benchmark text datasets of different sizes and complexities are evaluated. Analysis with k-mean shows that particle swarm optimization with length feature weight and dynamic reduction produces the optimal outcomes for almost all datasets tested. This paper provides new alternatives for text mining community to cluster text documents by using cohesive and informative features. [ABSTRACT FROM AUTHOR]

• The optimised fMACDH indicator and fMACDH-fRSI indicator are proposed in the paper. • The two trading indicators with GA can improve performance of portfolio management. • Two rule-based portfolio rebalancing algorithms are proposed with these indicators. Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1–5 days ahead as the prediction depth, using 1–5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26 % − 27 %; as well outperforms the Buy and Hold strategy by 5 % − 40 %. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54 % − 55 %; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166 %. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing. [ABSTRACT FROM AUTHOR]

• Counterfactual example-based explanation. • Explainable bankruptcy prediction model. • Feature-weighted multi-objective counterfactuals. • GA-based counterfactual generation algorithm. In recent years, there have been many studies on the application and implementation of machine learning techniques in the financial domain. Implementation of such state-of-the-art models inevitably requires interpretability for users to understand the result and trust. However, as most of the machine learning methods are "black-box," explainable AI, which aims to provide explanations to users, has become an important research issue. This paper focuses on explanation by counterfactual example for a bankruptcy-prediction model. Counterfactual-based explanation offers an alternative case for users in order for them to have a desired output from the model. This paper proposes a genetic algorithm (GA)-based counterfactual generation algorithm using feature importance whilst taking other key factors into account. Feature importance was derived from a prediction model, and key factors for counterfactuals include closeness to the original dataset and sparsity. The proposed method presented advantages over the nearest contrastive sample and a simple counterfactual generation algorithm in the experiment. Also, it provides relevant and compact explanations to enhance the interpretability of the bankruptcy prediction model. [ABSTRACT FROM AUTHOR]

This paper investigates the clustered shortest-path tree (CluSPT) problem, a generalized network design problem that has several applications in various areas such as network design, agricultural irrigation settings, distribution of goods and products, etc. The main characteristic of the investigated problem is its hierarchical structure with the nodes of the underlying graph split into a given number of clusters. Its aim is finding a shortest-path spanning tree from a predefined source node to all the other nodes of the graph, with the property that every cluster should generate a connected subgraph. In this paper, we present two different two-level genetic algorithms (GAs) for solving the clustered shortest-path tree problem, in which, a framework with a macro-level sub-problem is used to generate trees spanning the clusters using two different Cayley encodings. The encodings in question are Prüfer and Dandelion codes. This sub-problem is coupled with a micro-level sub-problem that determines the best corresponding feasible solution of the CluSPT problem associated to a given tree spanning the clusters by means of a dynamic programming algorithm. Extensive computational results are stated and interpreted on the two sets of benchmark instances available from the literature and containing 452 non-euclidean instances. The achieved results prove that our novel two-level based genetic algorithms are highly competitive against the existing solution approaches from the specialized literature, more precisely, 146 best known solutions were equaled and new improved solutions were found for 292 out of 452 benchmark instances. Between our proposed solution approaches the differences are not remarkable for small instances, but when the size of the problem grows, the GA based on Dandelion code outperforms the GA based on Prüfer code, being better suited to evolutionary search due to its locality and heritability properties. • We focus on the clustered shortest-path tree (CluSPT) problem. • We present two different two-level genetic algorithms for solving the problem. • The problem is decomposed into two sub-problems that are solved separately. • The results prove that our algorithms are competitive against existing approaches. [ABSTRACT FROM AUTHOR]

• First large-scale framework to accommodate multivariate pair formation. • Formulates pair formation as a Mixed Integer Programming model. • NSGA-II simultaneously optimizes volatility and mean-reversion. • Details genetic algorithm alterations for implementation. • Significantly outperforms benchmark strategies. Forming combinations of comoving assets is a critical step in pairs trading that has only been addressed either manually or through enumerative procedures. Both approaches fail in the multivariate case and do not consider conflicting objectives in the problem structure. This paper is the first attempt to address these novel problems by presenting an intelligent system that recommends profitable pair combinations through a Mixed Integer Programming (MIP) formulation and solving the NP-Hard optimization problem with a multi-objective genetic algorithm (NSGA-II) containing problem specific modifications. Combinations of assets are optimized on two conflicting objectives of risk (mean-reversion) and return (spread variance) to form sets of profitable multivariate pairs trading opportunities. Promising results support the superiority of multi-objective and multivariate pairs trading strategies over their traditional single objective and univariate counterparts. The findings should motivate new directions for pairs trading research and also expand the applications of evolutionary multi-objective optimization for hard problems in finance and other industries. [ABSTRACT FROM AUTHOR]

• Fast learning and data imputation using neighbour artificial immune system with a genetic algorithm. • Sustained and reliable predictions as the number of missing data increases in the datasets. • Sustained or improve recommendations of items to users from common methods used in collaborative filtering as missing data increases. In collaborative filtering, one of the main challenges that researchers face is sparseness in the data, which is caused by users rating fewer items as the number of items increase in the dataset. The effect is poor predictions and recommendations of items to users by expert and intelligent systems like the recommender system. This paper proposes a Nearest Neighbour Artificial Immune System with a Genetic Algorithm (NNAISGA) to perform fast data imputations to reduce sparseness. The main impact is sustained and reliable predictions as the number of missing data increases in the dataset. The second benefit is to help sustain or improve recommendations of items to users from common methods used in collaborative filtering such as the User-based, Item-based, Slope-one, Tendencies-based and Non-Negative Matrix Factorisation (NNMF) methods. We show that NNAISGA is the desired method for learning and imputation over the traditional genetic algorithm. The findings show that using the NNAISGA as a fast imputation method yields promising results. All of the methods, except for the User-based method, show significant improvements or sustained accuracies in terms of predictions and recommendations. [ABSTRACT FROM AUTHOR]

To reduce energy consumption (EC) in the wastewater treatment process (WWTP) for achieving energy conservation and emission reduction, multi-objective evolutionary algorithm (MOEA) applied in WWTP needs to have better convergence and diversity. In this paper, a non-dominated sorting genetic algorithm III based on utopian point improvement (NSGAIII-UP) is proposed, which overcomes the disadvantage that multi-objective evolutionary algorithm based on utopian point decomposition (MOEA/D-UP) cannot handle the multi-segment discontinuous Pareto front (PF) problem well. By comparing the proposed NSGAIII-UP with 8 advanced algorithms by 2 metrics on 22 benchmark functions, the effectiveness of the proposed algorithm in dealing with the complex PF problems is proved. Moreover, a multivariable model predictive controller (MMPC) is used to control the dissolved oxygen and nitrate nitrogen concentrations in WWTP, in which NSGAIII-UP is applied to the upper EC optimization of WWTP. The simulation results show that compared with the closed-loop control, the EC in the WWTP is reduced by 7.395 %, and the lowest EC is achieved compared with the other 8 algorithms. It helps to reduce energy waste in WWTP and promotes ecological sustainability. The source code of NSGAIII-UP is publicly available at https://github.com/sut16/NSGAIII-UP. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, a growing number of applications are migrating to a cloud environment. In the process, the real-time scheduling of workflows has gradually become a technical challenge, due to the dynamic and uncertain nature of cloud environments and the complex dependencies between sub-tasks of the workflow. Although various methods have been reported up to now, these methods have their respective shortcomings, such as heuristic-based methods are hard to find optimal scheduling scheme and metaheuristic-based methods incur high computational overhead, which often lead to the violation of QoS (Quality of Service) requirements and increases service renting costs of executing workflows. Inspired by the successful application of Deep Reinforcement Learning (DRL) in cloud job scheduling, this paper proposes a real-time workflow scheduling method which combines Genetic Algorithm (GA) and DRL, aiming to reduce both execution cost and response time. To be specific, we design a real-time workflow scheduling algorithm named GA-DQN by combining the global search capability of GA and the environment awareness decision-making capability of DRL to divides scheduling process into two stages. First, the execution scheme of workflow in virtual machine is calculated when workflow arrives. Then, a DRL agent uses this scheme as the feature of workflow to assign workflow to a suitable virtual machine. In this way, the use of DRL to sense environment increases the computational efficiency of GA, and the execution scheme obtained by GA helps DRL to obtain the feature of workflow. On this basis of real world workflow, three groups of simulation experience are carried out to compare GA-DQN with four baseline method which consist of three traditional methods and a state-of-the-art method. The comparison results demonstrate that GA-DQN outperforms the other methods in terms of response time, execution cost, and success rate across different workloads and cloud instance configurations. [ABSTRACT FROM AUTHOR]

Surrogate evaluation is a useful, if not the unique, technique in population-based evolutionary algorithms where exact fitness calculation is too expensive. This situation arises, for example, in Genetic Programming (GP) applied to evolve scheduling priority rules, since the evaluation of a candidate rule amounts to solve a large number of problem instances acting as training set. In this paper, a simplified model is proposed that relies on finding and then exploiting a small set of small problem instances, termed filter, such that the evaluation of a rule on the filter may help to estimate the performance of the same rule in solving the training set. The problem of finding the best filter is formulated as a variant of the optimal subset problem, which is solved by means of a Genetic Algorithm (GA). The surrogate evaluation of a new candidate rule consist in solving the instances of the filter. This model is exploited in combination with a Memetic Genetic Program (MGP); the resulting algorithm is termed Surrogate Model MGP (SM-MGP). An experimental study was performed on the problem of scheduling a set of jobs on a machine with varying capacity over time, denoted (1 , C a p (t) | | ∑ T i). The results of this study provided interesting insights into the problems of filter and rules calculation, and showcase that the priority rules evolved by SM-MGP outperform those evolved by MGP. • Formalization of the Optimal Filtering Set Problem (OFSP). • Development of a Genetic Algorithm (GA) for solving the OFSP. • Combination of surrogate evaluation and local search in Genetic Programming. • Analysis and comparison of eleven variants of Genetic Programming on performance. [ABSTRACT FROM AUTHOR]

Text augmentation is a popular technique in natural language processing (NLP) that has been shown to improve the performance of various downstream tasks. The goal of text augmentation is to generate additional training data from existing data, thereby increasing the amount of data available for training machine learning models. In this paper, we propose a novel approach to text augmentation that combines the power of graph-based neural networks and genetic algorithms. Our proposed scheme, called GTR-GA, is designed to generate diverse and high-quality augmented text data by exploring the high-dimensional feature space of text data using graph-based neural networks and genetic algorithms. Our approach utilizes a graph attention network (GAT) based model, called HetGAPN, to learn node representations in a heterogeneous graph representing text data. We introduce the concept of node aggregation and bidirectional attention in HetGAPN to better capture the relationships between different features of the input text data. We then use a genetic algorithm to generate a diverse set of candidate embeddings that explore different parts of the input space. Our approach uses perplexity as the objective function to evaluate the quality of the augmented text data generated by each candidate embedding. The most fit candidate embeddings are selected as parents, and crossover and mutation operators are used to generate a new population of candidate embeddings. Our experimental results show that GTR-GA is capable of generating high-quality augmented text data that improves the performance of various downstream NLP tasks, such as sentiment analysis and text classification. The proposed scheme can be applied to a wide range of NLP tasks and can help overcome the data scarcity problem that is often encountered in NLP. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology based on fuzzy cognitive maps (FCMs) and metaheuristic algorithms to generate prescriptive models, called PRescriptiVe FCM (PRV-FCM). FCMs are a set of concepts interrelated that describe the behavior of a system. This kind of modeling has been extensively used to build descriptive and predictive models. We propose an extension of FCMs to develop prescriptive models and support decision-making in different domains. This adaptation characterizes FCMs, using system and prescriptive concepts. After that, it uses a metaheuristic algorithm (in this case, we use a genetic algorithm) to optimize prescriptive concepts based on system concepts and the stability of the FCM. Our proposed prescriptive approach was implemented and tested in four scenarios where it demonstrated its capability to find solutions that lead to desired values for the variables of interest. Specifically, no significant differences were found between the values of the prescriptive variables in the datasets and those generated by PRV-FCM. [ABSTRACT FROM AUTHOR]

Numerous finance-based projects encounter disturbances from a variety of uncertain factors during their execution, under which both the original baseline schedule and financing alternative become infeasible. However, many previous studies on the finance-based project scheduling problem (FBSP) neglected a potential feature of stochastic activity durations. In this paper, we address the issue of generating a robust project schedule that not only satisfies financing credit and renewable resource limits, but also tackles disruptions due to activity uncertainty. A bi-objective model for finance-based and resource-constrained robust project scheduling problem (FBRCRPSP) is constructed, where the trade-off between profit and robustness is considered. Based on the transformed integer programming model, an exact procedure of ε -constraints is proposed to obtain Pareto-optimal solutions for small-sized instances. For large-scale projects, a non-dominated sorting genetic algorithm with local search (NSGA-II-LS) that deeply explores the neighboring solution space is developed, in which a generic procedure with two new recursion policies is proposed to determine the robustness of schedules. Benchmarking is conducted to evaluate the efficiency of the algorithms via some performance criteria. The results show that all developed approaches have good performance in small-sized instances, and the NSGA-II-LS outperforms the non-dominated sorting genetic algorithm without local search (NSGA-II) in terms of the spread, convergence, and diversity of Pareto-optimal solutions significantly. In addition, some managerial insights are summarized to enlighten project managers. [ABSTRACT FROM AUTHOR]

Transportation, telecommunication, electricity supply, and production–distribution systems all involve the construction and management of highly sophisticated networks. Without properly designing such networks, none of these systems work effectively. Network design problems, as the core of network-based decision making systems, can be used to model the technological design of networks. They are widespread and have many industrial applications. In telecommunication, usually an infrastructure is sought, as a combination of possible links and switches, which can best maintain a given level of data traffic between a number of servers and devices. This paper presents a solution to a problem in which in every period of time, the specification of some of the nodes in network may need to be altered. In the current procedure, all arcs and nodes are fixed and only the node parameters need to change. However, the presented solution strategy, because of its multi-layered characteristic, can be easily extended to the cases where arcs and nodes are also subject to change. The solution procedure has been tested on the cloud-based Internet Café problem. In this problem, different types of video games can use different amount of RAM, CPU, GPU, and video-RAM resources, with demand for each type of game varying dynamically in a specified period, e.g., over a day. Different servers provide these resources to dumb-terminals which act essentially as input–output devices, including display monitors, for games running on the servers. Each server needs to provide the necessary resources for the games currently played on all terminals served. In this way, depending on the demand of each terminal, at each period, the terminal's capability can change dynamically, and it will act as a different virtual machine in different periods. Hence, the requirements of the Internet Café infrastructure change dynamically over time. The solution procedure has been tested on a series of generated large instances, and the results are very promising. For a total of 9 cases tested, the optimal solution is obtained, in a matter of minutes, for 3 cases. For an additional 2 cases, the solution obtained is within less than 2% of the optimal solution. The results are also reported for 3 additional test cases for a problem with over 13,000 users and 4,000 seats, demonstrating the procedure capability in handling extra-large problems. • A novel genetic algorithm has been proposed for network design problems. • The solution procedure is implemented for the cloud-based Internet Café problem. • The procedure is self-adaptive and embeds some decision parameters into genomes. • The procedure can find optimal solution for some very large instances in minutes. [ABSTRACT FROM AUTHOR]

Crowdsourcing offers the prospect of a more open and socialized approach to product development by bringing together crowdsourcing community resources and connecting open innovation participants. Doing this effectively involves finding an optimal combination of crowdsourcing resources. However, the crowdsourcing community typically focuses on realizing a wide variety of different micro-tasks to meet overall customer requirements, giving rise to numerous potential ways of combining service resources. The parties making up different crowdsourcing community platforms also have various kinds of privacy preferences, making it hard for operators to disclose all of the information relevant to the development of a particular product. This paper proposes a privacy-aware strategy for the optimal combination of crowdsourcing product development resources based on edge computing and cloud computing. Product development application scenarios are examined that envisage the deployment of privacy-aware edge computing and collaboration between a central cloud and edge-based crowdsourcing communities. The crowdsourcing community environment and its privacy risk model are analyzed and the construction and presentation of cloud crowdsourcing platforms for complex tasks are studied. A mechanism for uncovering the optimal cross-platform combination of crowdsourcing product development services based on privacy-aware cloud-edge collaboration is then elaborated, together with a technique for implementing the proposed mechanism via a cooperative bacteria foraging optimization (CBFO) algorithm. Random test data was obtained based on a cloud crowdsourcing platform and two sets of validation tests were undertaken that show that the proposed method can preserve the privacy of crowdsourcing communities, while efficiently solving the problem of the cross-platform mobilization of crowdsourced product development resources. The approach is compared with more basic bacteria foraging optimization (BFO) and genetic algorithm (GA)-based techniques and it is found that CBFO offers better computational performance when confronted with convergence and other factors that can influence the resource combination optimization model, such as a change in the number of micro-tasks. [ABSTRACT FROM AUTHOR]

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs are among the biggest sources of expenditure in company activities, it is of great significance to optimize this in the Home Health Care industry. From the perspective of optimizing the cost of transportation, this paper studies the Vehicle Routing Scheduling problem as it applies to HHC companies. According to a survey of the HHC companies, during the process of delivering medication drugs, the quantity of drugs required for each patient is non-deterministic when the company makes planned routes. This paper considers uncertain demand as a fuzzy variable, which is closer to a potential real life scenario. A Home Health Care Scheduling Problem with fuzzy demand is considered and a fuzzy chance constraint model is designed. We propose a hybrid genetic algorithm integrated with stochastic simulation methods to solve the proposed model. Firstly, the problem is reduced to the classical vehicle routing problem within a time window. Experimental results for Solomon’s and Homberger’s benchmark instances show that the proposed algorithm performs efficiently. Then other experiments on the fuzzy version model are undertaken with the variable value of the Dispatcher Preference Index (DPI) parameter between [0, 1]. Finally, the influence of DPI on the final objective and the indicators of the problem are discussed using stochastic simulation, and the best value of DPI is obtained. This research will help HHC companies to make appropriate decisions when arranging their vehicle scheduling routes. [ABSTRACT FROM AUTHOR]

Recombined fingerprints have been suggested as a convenient approach to improve the efficiency of anonymous fingerprinting for the legal distribution of copyrighted multimedia contents in P2P systems. The recombination idea is inspired by the principles of mating, recombination and heredity of the DNA sequences of living beings, but applied to binary sequences, like in genetic algorithms. However, the existing recombination-based fingerprinting systems do not provide a convenient solution for collusion resistance, since they require “double-layer” fingerprinting codes, making the practical implementation of such systems a challenging task. In fact, collusion resistance is regarded as the most relevant requirement of a fingerprinting scheme, and the lack of any acceptable solution to this problem would possibly deter content merchants from deploying any practical implementation of the recombination approach. In this paper, this drawback is overcome by introducing two non-trivial improvements, paving the way for a future real-life application of recombination-based systems. First, Nuida et al.’s collusion-resistant codes are used in segment-wise fashion for the first time. Second, a novel version of the traitor-tracing algorithm is proposed in the encrypted domain, also for the first time, making it possible to provide the buyers with security against framing. In addition, the proposed method avoids the use of public-key cryptography for the multimedia content and expensive cryptographic protocols, leading to excellent performance in terms of both computational and communication burdens. In fact, after bootstrapping, the merchant is no longer required to participate in any file transfer, reducing the investment in equipment required for content distribution to the bare minimum. The paper also analyzes the security and privacy properties of the proposed system both formally and informally, whereas the collusion resistance and the performance of the method are shown by means of experiments and simulations. [ABSTRACT FROM AUTHOR]

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning; none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges' states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a perspective is fundamental to learning based algorithms design for max-cut and other graph partitioning problems, and can shed lights on future researches. Furthermore, since max-cut is one of the most classic and fundamental NP hard problems, many real-world problems involve dividing graph data into different parts to optimize certain functions, this new perspective may inspire related or similar problems. Secondly, besides the original edge-states based perspective, and the post-flip edge-states learning (PFESL) operator based on it, our memetic algorithm also incorporates a novel evolutionary framework which alternates between EDA based Iterated Tabu search (ITS) and path relinking based genetic algorithm. Finally, the proposed algorithm provides competitive results on two mostly used benchmark sets and improves the best-known results of 6 most challenging instances. • An original edge-state perspective for design learning operators for max-cut. • A novel learning operator to learn edge states. • A new evolutionary framework for max-cut. • It provides competitive results on two mostly used benchmark sets. [ABSTRACT FROM AUTHOR]

Surface roughness prediction is important for polishing with high production efficiency and low cost since it can reduce the time required for trials and testing. Although many prediction methods have been proposed, there are no studies focusing on the surface roughness prediction in multi-jet polishing (MJP) process. This paper presents the development of a robust surface roughness prediction model based on ensemble learning with a genetic algorithm (ELGA), that can be used for the prediction of surface roughness in the MJP of 3D-printed 316L stainless steel. This paper firstly provides a brief review of the current status of the surface roughness prediction method. Then the development of the ELGA surface roughness prediction model is introduced in detail, which consists of four modules, namely pre-processing module, multi-algorithm regression module, GA module and ensemble module. After that, a series of MJP experiments on 316L stainless steel are conducted to test the effectiveness of the proposed ELGA method. Moreover, the ELGA is compared with six other machine learning-based surface roughness prediction models based on the experiment results. Comparison results show that ELGA model yields the lowest average MAE for the surface roughness prediction in MJP. [ABSTRACT FROM AUTHOR]

Support Vector Machines are models widely used in supervised classification. The classical model minimizes a compromise between the structural risk and the empirical risk. In this paper, we consider the Support Vector Machine with feature selection and we design and implement a bi-objective evolutionary algorithm for approximating the Pareto optimal frontier of the two objectives. The metaheuristic is based on the non-dominated sorting genetic algorithm and includes problem-specific knowledge. To demonstrate the efficiency of the algorithm proposed, we have carried out extensive computational experiments comparing the Pareto-frontiers given by the exact method AUGMECON2 and the metaheuristic approach respectively in a set of well known instances. In this paper, we also discuss some properties of the points in the Pareto frontier. • The SVM with feature selection problem is considered from a bi-objective perspective. • A metaheuristic to approximate the Pareto front has been designed and implemented. • A computational experiment demonstrates the efficiency of our proposal. • Some properties of the points of the Pareto frontier are also discussed. [ABSTRACT FROM AUTHOR]

This paper studies a multi-project scheduling problem sharing multi-skilled staff (MSMPSP). In a distributed decision-making environment, each project is independently scheduled by its project managers, while multiple projects compete for limited staff with multiple skills. A two-stage decomposition model, including an initial local scheduling stage and a global coordination stage, is established to describe this problem. Then, a two-stage approach with softmax scoring mechanism (TSA-SSM) is proposed to solve the local schedule of minimizing the single-project makespan and the global coordination decision of minimizing the total tardiness cost (TTC). According to the local scheduling plan obtained by the forward–backward scheduling genetic algorithm (FBSGA), the softmax scoring mechanism uses a greedy assignment strategy to solve the resource conflicts in the global decision stage, which is combined with the characteristics of multi-skilled staff. Based on the collection of multi-project instances, some numerical experiments are carried out. The results show that our TSA-SSM gets better solutions to large-size and strong conflict instances than some distributed and centralized methods, which proves that our method can effectively coordinate the allocation of multi-skilled staff among multiple projects. In addition, further experiments show that our method is also suitable for solving de-coupled problems, and satisfactory results are obtained. • A two-stage model is established for the problem studied in this paper. • A two-stage approach with softmax scoring mechanism (TSA-SSM) is developed. • TSA-SSM performs better than compared methods for most of the problem subsets. • TSA-SSM is further verified by solving the de-coupled problems. [ABSTRACT FROM AUTHOR]

• Proposition of a new feature for the Biased Random-Key Genetic Algorithm. • Identification of lower and upper bounds, as well as some optimal values for classical instances. • Developed genetic algorithm are strong contenders for large scale problems. In this paper, we advance the state of the art for solving the Permutation Flowshop Scheduling Problem with total flowtime minimization. For this purpose, we propose a Biased Random-Key Genetic Algorithm (BRKGA) introducing on it a new feature called shaking. With the shaking, instead to full reset the population to escape from local optima, the shaking procedure perturbs all individuals from the elite set and resets the remaining population. We compare results for the standard and the shaking BRKGA with results from the Iterated Greedy Search, the Iterated Local Search, and a commercial mixed integer programming solver, in 120 traditional instances. For all algorithms, we use warm start solutions produced by the state-of-the-art Beam-Search procedure. Computational experiments show the efficiency of proposed BRKGA, in addition to identify lower and upper bounds, as well as some optimal values, among the solutions. [ABSTRACT FROM AUTHOR]

Highlights • The PIMA Indian Type-2 diabetes dataset is used. • Pre-processing techniques are combined together to access high-quality data. • Significant features are found using SVM. • Four bi-objective meta-heuristics are employed to maximize the accuracy and to minimize the number of selected features. • The 10-fold cross validation method is used to validate the constructed model. Abstract Diabetes mellitus is a medical condition examined by data miners for reasons such as significant health complications in affected people, the economic impact on healthcare networks, and so on. In order to find the main causes of this disease, researchers look into the patient's lifestyle, hereditary information, etc. The goal of data mining in this context is to find patterns that make early detection of the disease and proper treatment easier. Due to the high volume of data involved in therapeutic contexts and disease diagnosis, provision of the intended treatment method become almost impossible over a short period of time. This justifies the use of pre-processing techniques and data reduction methods in such contexts. In this regard, clustering and meta-heuristic algorithms maintain important roles. In this paper, a method based on the k-means clustering algorithm is first utilized to detect and delete outliers. Then, in order to select significant and effective features, four bi-objective meta-heuristic algorithms are employed to choose the least number of significant features with the highest classification accuracy using support vector machines (SVM). In addition, the 10-fold cross validation (CV) method is used to validate the constructed model. Using real case data, it is concluded that the multi-objective firefly (MOFA) and multi-objective imperialist competitive algorithm (MOICA) with a 100% classification accuracy outperform the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) with the accuracies of 98.2% and 94.6%, respectively. [ABSTRACT FROM AUTHOR]

Highlights • Feature selection over high-dimensional colon cancer Microarray Datasets. • Features selected from both Gene Expression and DNA-Methylation Microarray datasets. • Resultant six biomarker genes for colon cancer validated using Enrichment Analysis. • Biomarker genes validated on independent datasets with 99.9% classification accuracy. Abstract Cancer is a dangerous disease that causes death worldwide. Discovering few genes relevant to one cancer disease can result in effective treatments. The challenge associated with the Microarray datasets is its high dimensionality; the huge number of features compared to the modest number of samples in these datasets. Recent research efforts attempted to reduce this high-dimensionality using different feature selection techniques. This paper presents an ensemble feature selection technique based on t -test and genetic algorithm. After preprocessing the data using t -test, a Nested Genetic Algorithm, namely Nested-GA, is used to get the optimal subset of features by combining data from two different datasets. Nested-GA consists of two Nested Genetic Algorithms (outer and inner) that run on two different kinds of datasets. The Outer Genetic Algorithm (OGA-SVM) works on Microarray gene expression datasets, whereas the Inner Genetic Algorithm (IGA-NNW) runs on DNA Methylation datasets. Nested-GA is performed on a colon cancer dataset with 5-fold cross validation. After applying Nested-GA, the Incremental Feature Selection (IFS) strategy is used to get the smallest optimal genes subset. The genes subset has been validated on an independent dataset resulting in 99.9% classification accuracy. Consequently, the biological significance of the resulting optimal genes is validated using Enrichment Analysis. Moreover, the results of Nested-GA have been compared to the results of other feature selection algorithms that have been run on either Gene Expression or DNA Methylation datasets. From the experimental results, Nested-GA showed the highest classification performance with a small optimal feature subset compared to the other algorithms. Furthermore, by running Nested-GA on lung cancer datasets that contain two different cancer subtypes, it resulted in significantly better classification accuracy (98.4%) compared to the accuracy of a previous research (84.6%) that utilized lung cancer DNA-Methylation data only. [ABSTRACT FROM AUTHOR]