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Traditional identification methods make it difficult to accurately determine the parameters of the friction model. Based on the LuGre friction model, a hybrid optimization algorithm (HO) is presented to address such aspects. This algorithm combines the genetic algorithm (GA) and particle swarm optimization algorithm (PSO) to obtain the globally optimal solutions. It incorporates three main improvements: introducing selection and crossover operations into the PSO algorithm, utilizing a variable inertia weight coefficient, and applying variable learning coefficients for enhanced performance. Compared to the static optimal solutions of the GA and PSO algorithms, the HO algorithm can improve the optimal solution by 40.68 % and 35.89 % respectively. The HO algorithm can achieve the highest model accuracy: the maximum identified friction error with the HO algorithm is only 1.70 kN, compared to 3.69 kN with the GA method and 8.52 kN with the PSO algorithm. Furthermore, a novel adaptive friction compensation controller (AC) is introduced based on the identification results to improve the stability and accuracy of the electro-hydraulic proportional systems of a 23-ton robotic excavator. Comparisons with existing proportional integral differential (PID) and feedforward compensation controllers (FC) highlight the superiority of the proposed adaptive controller in terms of trajectory accuracy and robustness. In sinusoidal trajectory tracking experiments, the AC method can achieve the highest tracking performance among the three controllers, and the maximum tracking error is only 11.56 mm. Compared to the FC controller, the control accuracy with the AC controller can be improved by 43.38 %. Experiments and simulations have shown that the friction compensation controller can effectively mitigate friction and other disturbances. As a result, the phenomena of crawling and flat peak phenomena can be eliminated. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as a promising paradigm for distributed data processing, but managing numerous devices in fog domains is complex due to the scale of the infrastructure. To address this challenge, organizing fog devices into fog colonies allows independent management on a smaller scale. We present a genetic algorithm (GA) approach that utilizes hierarchical clustering to define the fog colony layout. The GA selects a subset of colony candidates from the dendrogram obtained with hierarchical clustering and optimizes the network communication time between users and applications and the execution time of algorithms that manage application placement in each colony. We deployed an NSGA-II, a multi-objective approach for GAs, to evaluate our proposal. Our experimental results demonstrate that combining a GA with hierarchical clustering improves both optimization objectives. We conducted nine experiment scenarios, varying the number of applications and fog devices. Our results show that even in the worst-case scenario, the GA's results dominated the solutions obtained by two control algorithms after only 137 generations. Additionally, the number of genetic solutions and their homogeneous distribution in the Pareto front were satisfactory. • Definition of a genetic algorithm for optimization of the fog colony layout. • Solution space design based on the hierarchical clustering of the infrastructure. • Adaptation of genetic operators for solutions represented as labeled dendrograms. • Hybrid fitness evaluation with a greedy algorithm for service placement. • Results support the coordinated optimization of service placement and colony layout. [ABSTRACT FROM AUTHOR]

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption. [ABSTRACT FROM AUTHOR]

Early detection of faults in photovoltaic (PV) systems is crucial for improving their lifespan and reliability. Conventional protection devices may not be able to detect electrical faults under critical conditions. This usually occurs due to (i) the current-limiting nature and non-linear output characteristics of PV arrays, (ii) the changing environmental conditions (such as irradiance and temperature variations), and (iii) the influence of the maximum power point tracker (MPPT), particularly in the presence of critical fault impedance values or mismatch levels. Therefore, modern data-driven methods are required for fault detection and classification in PV arrays. However, careful investigation in previous studies reveals the existing gaps and limitations, such as poor accuracy, and incomprehensive models which have not considered various electrical faults, low mismatch levels, and critical fault impedance values. To this end, in this study, a comprehensive multilayer model is proposed which consists of six layers for diagnosis, classification and severity identification of electrical faults in PV arrays. Each layer adopts a weighted ensemble learning (WEL) algorithm consisting of three classifiers namely Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR). Besides, a genetic algorithm (GA) is employed in each layer to find the optimal weights for each classifier (i.e., SVM, NB and LR). The initial dataset is acquired through an investigation into the PV array current–voltage (I-V) characteristic curve and extraction of several features under various faulty and normal conditions. To reduce the dataset dimensionality thus computationally simplifying the training process, the sequential floating forward selection (SFFS) algorithm is utilized in each layer as a powerful feature selection technique. The results show a highly accurate performance in fault diagnosis, classification, and severity assessment, with an average simulation and experimental accuracies of 98.9% and 98.37%, respectively. [ABSTRACT FROM AUTHOR]

In the defense industry, outsourcing has become crucial during production stages, primarily because certain tasks involve complex operations that may exceed the capabilities of in-house machinery. This is often influenced by factors such as technical competencies and high production costs. On the other side, recent shifts in climate patterns and growing environmental concerns have led to a significant impact on scheduling decisions, especially influenced by the carbon footprint of production factories. Therefore, it is important for manufacturing factories to evaluate both economic and environmental costs. A Mixed Integer Programming (MIP) model is formulated to solve the real-life production scheduling problem including machine costs, operation outsourcing and carbon footprint of the schedule with minimizing total costs. Since the mathematical model has difficulty in finding solutions as the problem size increases, a Hybrid Genetic Algorithm (HGA) which combines Genetic Algorithm (GA) with Iterated Local Search (ILS) procedure is proposed to solve especially medium-sized and large-sized problems. The efficiency of the proposed algorithm is demonstrated through randomly generated test problems. Obtained results indicate the superiority of the proposed HGA over GA, showcasing its ability to generate high-quality solutions and reduce overall costs in a reasonable solution times. [ABSTRACT FROM AUTHOR]

In this study, in order to cope with the uncertain environments and space interference scenarios encountered in the production process for a class of manufacturing projects, we propose a novel manufacturing project scheduling problem with setup times under dynamic and interference environments (MPSPST-DIE), and design a novel multi-surrogate genetic programming hyper-heuristic (HH-MGP) algorithm to address it. Firstly, MPSPST-DIE is required to make decisions on the activity schedule, resource setup and space allocation. Therefore, we modify the traditional resource based policy class that only contains the activity schedule and simulate the entire scheduling process. Secondly, a new hyper-heuristic genetic programming algorithm is designed to automatically evolve activity rules, resource setup rules and space allocation rules simultaneously. Moreover, the multi-surrogate is devised to improve the performance of the basic genetic programming (GP) algorithm. In addition, a new evolutionary learning mechanism is embedded in the multi-surrogate. Different surrogates learn from each other to complement each other's strengths. Finally, numerical instances of the MPSPST-DIE are generated by configuring specific parameters of spatial resources and extensive numerical experiments are performed. At the same time, we implement the Taguchi experiment for the sensitivity analysis of parameters. The comparative analysis between the HH-MGP and traditional rules is performed. Further, the performance comparison between the multi-surrogate and other surrogates is also conducted. The experimental results show that the evolved rule of the HH-MGP performs better than the traditional rules for the MPSPST-DIE. The performance of the multi-surrogate model added to the GP algorithm are generally better than the single-surrogate model and no-surrogate. • A novel MPSP with setup times under dynamic and interference environment is proposed. • Traditional RB policy is modified and the entire scheduling process is simulated. • A new GP is designed to automatically evolve three types of rules simultaneously. • A multi-surrogate model is devised to improve the performance of GP. • A new evolutionary learning is embedded in multi-surrogate models. [ABSTRACT FROM AUTHOR]

In manufacturing industries, including the wood industry, devices, and equipment are considered the basic elements and the main capital for production. That is why managers are trying to maintain and use these devices and equipment optimally. On the other hand, repurchasing device parts or repairing equipment in case of major damage can cause more damage than planned costs. Therefore, a model that can determine the fault class based on the signs seen in the equipment would prevent major damage to the device and save on repair costs. In this regard, using the registered features for equipment and with the help of machine learning algorithms, a model can be created that can classify devices in the appropriate class based on their observed features. The present study uses nine machine learning algorithms to make this model, trains each model on three sets of selected features, and finally compares them. It is worth mentioning that after evaluating the models, based on the features selected from the embedded techniques, permutation feature importance methods, and genetic algorithm, the best models are considered as categorical boosting with the training and testing accuracy of 0.895 and 0.909, random forest with the training and testing accuracy of 0.905 and 0.893, and extreme gradient boosting with the training and testing accuracy of 0.884 and 0.885. [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]

The genetic algorithm (GA) is a well-known meta-heuristic technique for addressing the static mobile robot global path planning (MRGPP) issue. Current GA, however, has certain shortcomings, such as inefficient population initialization and low-quality solutions. As an enhanced GA, a Linear Rank-based, or Clearance-based Probabilistic Road Map (CBPRM), technique is proffered to overcome these difficulties. The new model guides the population initialization process by using the fitness score of each cell in the environment, lowering the number of infeasible pathways created. Furthermore, a genetic operator combination is proposed to balance the global and local search and increase the quality of the optimum path created in terms of path length and safety. Two experiments were carried out to assess the suggested GA. The novel population initialization strategy was compared to two current models in the first experiment, and the findings revealed that the suggested approach greatly decreases the number of infeasible pathways created and the time required for the process. The ideal genetic operator combination was determined in the second experiment, and the results revealed that the suggested combination improves the quality of the optimal path created in fewer iterations. In summary, the proposed GA improves on previous models by proposing a novel population initialization method and combining numerous genetic operators. These alterations improve the quality of the optimum path and indicate the suggested model's potential for solving the MRGPP challenge. • Proposed an enhanced GA for mobile robot global route planning. • Introduced a novel population initialization method based on fitness score. • Combined various genetic operators to balance global and local search. • Compared the proposed model with two existing state-of-the-art models. • Solving Mobile Robot Global Path Planning Challenge. [ABSTRACT FROM AUTHOR]

• Developed an Improved Particle Swarm Optimization (IPSO) model for optimizing heavy construction equipment management. • Integrated three critical objectives (i.e., time, cost, and quality) into a holistic equipment optimization process. • Demonstrated the versatility and effectiveness of the IPSO algorithm in managing complex, real-world construction scenarios. • Enhanced methodological transparency and reproducibility by detailing the software and hardware specifications used in the study. Expert systems play a crucial role in decision-making across various industries. This study introduces a novel expert system employing a tailored multi-objective optimization (MOO) model to address the intricate demands of highway projects. Integrating an advanced Enhanced Particle Swarm Optimization (IPSO) strategy, our model emphasizes key operations like excavation, hauling, grading, and compaction. Considering factors such as equipment count, velocity, and capacity, the system provides a comprehensive set of optimal solutions and reveals the Pareto frontier. In benchmarking our Improved Particle Swarm Optimization (IPSO) model against established methods, such as Genetic Algorithms (GA) by various researchers and the Guided Population Archive Whale Optimization Algorithm (GPAWOA), our approach significantly excelled. Our model demonstrated a 35.4%-time reduction and a 39.1% cost reduction while enhancing operational quality—starkly contrasting the modest improvements seen with other methods. This showcases the IPSO model's robustness in optimizing construction equipment utilization beyond current standards. Functioning as a decision-support tool, the expert system aids stakeholders in selecting optimal equipment setups, considering diverse attributes from equipment specifications to speed and volume. This application is exemplified through a real-world highway construction case study. The Case Study showcases the proposed model's remarkable impact, yielding a 35.4% time saving and a 16.8% cost decrease with Optimal Solution (I). In contrast, Optimal Solution (III) required 144.6% more time yet reduced costs by 39.1%. Furthermore, Optimal Solution (II) achieved a 4.6%-time reduction and a 32.4% cost decrease, demonstrating the system's versatility in managing constraints and optimizing decision-making processes across various project stages. [ABSTRACT FROM AUTHOR]

Recently, driven by advancements in the payload capacity and endurance of drones, the logistics industry has shown significant interest in drone Last-Mile logistics. Efficient routing are crucial scientific challenges in drone delivery problems. In this study, we address the routing problem in heterogeneous drone delivery, which involves a large drone transporting multiple small drones to sub-regions for parcel delivery, aiming to both reduce the drones' distance costs and improve customer satisfaction, termed HDDPBO To tackle the HDDPBO problem effectively, we propose a voting-based ensemble multi-objective genetic approach, named VEMOGA, in which an improved clustering algorithm is developed to divide customers into K clusters, enabling each drone to handle multiple parcel deliveries within a sub-region. In this way, it reduces the complexity of HDDPBO by transforming it into multiple sub-problems. Secondly, a multi-objective genetic approach with heuristic operators is proposed to explore high-quality solutions, in which customized crossover and mutation operators are designed in the genetic approach, and a voting-based ensemble algorithm is designed to robustly select the Pareto frontier with high-quality convergence and diversity. Extensive experiments are conducted on synthetic instances to evaluate the proposed algorithm, and the experimental results demonstrate superior performance compared to three other baselines. Additionally, a real-world instance has been scrutinized to ascertain the applicability of Last-Mile logistics, and sensitivity analyses of pivotal factors have been conducted and several managerial insights pertinent are given to the drone-based Last-Mile logistics. • A multi-objective model is proposed for heterogeneous drone logistics. • A voting-based ensemble multi-objective genetic algorithm is proposed. • The proposed algorithm achieves higher satisfaction and lower costs. [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]

The demand for accurate Multi-Output Spatio-temporal Forecasting is rising in areas like public safety, urban mobility, and climate variability. Traditional methods struggle with model calibration and data integration. This paper presents a methodological guideline for creating forecasting pipelines that handle multi-output forecasting complexities. Using a uniform methodology tested on three diverse datasets, the framework combines genetic algorithms and advanced models to optimize forecasting. Our evaluation shows significant performance improvements, with better adaptability to urban and rural datasets, aiding decision-making in spatio-temporal analysis. The framework achieved a 20% average improvement in the R 2 metric across all datasets, outperforming benchmark models. • Comprehensive and structured guide for developing Multi-Output forecasting models. • Three distinct datasets to demonstrate the method's adaptability to different data. • Employed a variety of deep learning models for time series forecasting. • Uses a Genetic Algorithm for optimizing model hyperparameters. • Applicable to real-world forecasting problems, with most data/code available. [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]

In the contemporary business landscape, accurately evaluating a company's financial health is essential for stakeholders to mitigate risks and avert bankruptcy. This study presents an innovative approach to improving business bankruptcy prediction through the hybrid integration of Domain Adaptation Learning (DAL) and Genetic Algorithm (GA) techniques. The hybrid model harnesses DAL to address distributional changes in real-world scenarios and utilises GA's proficiency in feature selection. Six machine learning models are rigorously evaluated against the proposed hybrid model: Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boosting (GB), k -Nearest Neighbours (k -NN), and Stacking Ensemble (SE). Our hybrid model performs well on imbalanced target datasets using the Area Under the Precision–Recall Curve metric: 0.93 (RF), 0.93 (SVM), 0.89 (LR), 0.91 (GB), 0.88 (k -NN), and 0.92 (SE). These findings highlight the model's ability to overcome the limitations of traditional approaches, offering a more reliable predictive framework for stakeholders to make informed decisions and proactively manage financial stability. Future research directions may explore the applicability of this hybrid model across different industries and the integration of additional techniques to further enhance its performance. • Hybrid GA-DAL model enhances bankruptcy prediction. • Integration of GA and DAL improves model adaptability. • Source–target domain creation for domain adaptation. • Feature standardisation using quantile transformation. • Six ML models were evaluated against a hybrid approach. [ABSTRACT FROM AUTHOR]

The alarming rates of biodiversity loss has draw attention of the scientific community in developing quantitative characterization methods of the state of life variety, ecosystem and habitats. One way to address such aspects involves acoustically monitoring the bird population, given its relation with the health and status of the ecosystem. Automated computational tools can be adopted to enhance the processing and interpretation of birdsongs. The performance and reliability of the system heavily depends on the input features extracted from the acoustic recordings. Numerous acoustical features for characterizing birdsongs are reported in the literature, however the determination of the most relevant ones remains elusive. Moreover, literature evidences a marked focus on classification or detection performance, providing limited details on the set of features that contribute to high performance. This study investigates the problem of discerning relevant acoustical features when dealing with the automatic classification of eight Colombian bird species. We adopt different audio signal processing techniques, namely temporal, spectral, cepstral and chroma analyses, to construct a heterogeneous set of features. Feature selection is implemented using principal components analysis, ReliefF feature ranking, and genetic algorithms. By considering nearest neighbors classifiers, support vector machines, neural networks and bayesian classifiers, 49 distinct machine learning models are tested. Selection schemes are fine-tuned looking for maximizing the classification performance. Our results demonstrate that effective machine learning classification of bird species can be achieved by refining a heterogeneous set of features through feature selection. Consistently, our work identifies Mel frequency Cepstral coefficients, spectral decrease, and spectral rolloff point as the most recurrent features across various feature selection schemes. By incorporating these features in a heterogenous subset totaling a minimum of 19 features, classification performances above 95% can be achieved using a nearest neighbor classifier with the Manhattan distance. These findings are particularly notable as they are derived from processing field-noise corrupted acoustic recordings. The outcomes of this study yield potential acoustical features suitable for the characterization of birdsongs. In addition, our methodological design may serve as a reference for extending the assessment of new features and machine learning models, thereby contributing to the development of effective and feasible conservation tools. • Heterogeneous acoustical features yield effective classification of bird species. • Candidate features for high-performance birdsongs classification are identified. • High classification accuracy and recall rate achieved with 19 heterogenous features. • Numerical experiments using birdsongs corrupted with various levels of field-noise. [ABSTRACT FROM AUTHOR]

An improved multi-island genetic algorithm (IMIGA) by integrating various common improvement methods, optimized through D-optimal and analysis of variance (ANOVA) techniques. IMIGA's performance is validated against various state-of-the-art algorithms across CEC2020 test suite and classic engineering problems, affirming its superiority. IMIGA's potential in industrial applications is demonstrated by employing it for optimal design in a micropositioning stage, leveraging machine learning (ML) for parameter optimization. With the optimal design parameters, driven by IMIGA and a surrogate ML model, the proposed stage attained a first natural frequency of 55 Hz and maximum strokes of 266.7 μm, 110.9 μm, and 0.7552 mrad in the x -, y -, and θ -directions, respectively. These experimental results closely align with the predicted outcomes from the design phase, highlighting the accuracy and reliability of the employed methodology. Such consistency between experimental measurements and predicted values underscores the efficacy of the design approach in achieving desired performance metrics. [ABSTRACT FROM AUTHOR]

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices. [ABSTRACT FROM AUTHOR]

The objective of this study is to craft a novel S-Box tailored to stringent security standards, achieved through iterative application of crossover and mutation operators inherent to real-coded genetic algorithms, ensuring robust image encryption. The designed S-Box is rigorously evaluated across multiple criteria, demonstrating its superiority over comparable S-Box designs found in existing literature. Furthermore, a secure image encryption method based on the designed S-Boxes devised and also including a 2D hyperchaotic Styblinski–Tang map. Thorough security assessments, encompassing statistical analysis and resilience testing against diverse attacks and noise, it is validated the encryption technique's efficiency and applicability across varied scenarios. • Proposed S-Box is robust against differential and cryptanalysis techniques. • The crossover and mutation operators real coded GA is used for the design S-Box. • S-Box has no fixed-points, reverse fixed-points and a short iteration ring. • Proposed encryption method includes the S-Box and a 2D chaotic map. • Cipher method provides in safeguarding sensitive information against cyber attackers. [ABSTRACT FROM AUTHOR]

This article considered the redundancy allocation problem (RAP) in repairable heterogeneous systems composed of k -out-of- n : G subsystems with a limited number of repairmen. The unit costs of the binary-state components within the subsystems remain constant over time. Exponential characteristics of independent failure and repair processes were assumed. To evaluate subsystem and system availability, a Continuous Time Markov Chain (CTMC) adapted to three standby modes (cold, warm, and hot) was developed. The characteristics of the redundant system and the assumption of a limited number of repairmen contribute to the existing scientific literature on RAP. Two nature-inspired metaheuristic approaches were developed to maximize the availability of the coherent system as an objective function to optimize redundancy with a cost constraint on component purchase. The proposed approaches were tested on a 15-unit large-scale system. The effectiveness and performance of the algorithms was evaluated as a function of the number of generations/iterations and population/swarm size. The genetic algorithm (GA) proved to be highly effective in finding the global optimum and outperformed particle swarm optimization (PSO) in terms of computational performance. Analysis of metaheuristic algorithms can offer valuable insights into future research on redundancy optimization. The sensitivity analysis of the large-scale system clearly demonstrates that the number of repairmen and cost constraints (up to three times the sum of the value of active components) have a significant impact on the system's availability. Above this level, increasing spending on system building, regardless of the number of repairmen, is not justified by considerations of system availability. [ABSTRACT FROM AUTHOR]

• Introducing SMDM for accurate dynamic landscape change detection. • Empirical comparison of SMDM with cutting-edge methods in change detection. • Incorporating SMDM into the HMRS framework to boost dynamic optimization. • Comparing proposed method to advanced methods in dynamic e-procurement. The increasing dynamism of global markets, coupled with the occurrence of unpredictable events, has introduced substantial challenges in formulating efficient supply chain strategies. The inherent dynamic nature of logistic networks necessitates a departure from traditional supply chain methodologies. This study proposes an advanced solution for dynamic e-procurement utilizing evolutionary algorithms (EAs). In conventional supply chains involving buyers and suppliers, a critical challenge is identifying cost-efficient suppliers capable of fulfilling consumer demands amidst fluctuating prices and quantities. Traditional optimization techniques often fail to perform effectively under these dynamic conditions. Moreover, detecting changes during the optimization process is an additional hurdle in dynamic optimization problems. Recent advancements have demonstrated the efficacy of EAs in solving a variety of real-world dynamic optimization issues. This research introduces a novel evolutionary algorithmic framework that integrates the Hybrid Multipopulational Reinitialization Strategy (HMRS), with a proposed hybrid change detection mechanism (named Smirnov-based Multi-sensor Detection Mechanism (SMDM)) to address the dynamic e-procurement problems. The proposed framework enhances the algorithm's adaptability and responsiveness to real-time changes within the e-procurement environment. By effectively detecting and responding to these variations, the framework aims to optimize procurement processes, ensuring efficiency and robustness in managing fluctuating requirements and conditions inherent to dynamic e-procurement scenarios. The empirical analysis presented underscores the superiority of Differential Evolution (DE) variants over Genetic Algorithm (GA) variants within the procurement context. The detailed empirical study validates the effectiveness of the proposed dynamic approach in addressing the challenges associated with dynamic e-procurement. Considering real-world parameter fluctuations, the proposed approach demonstrates significant resilience, positioning it as a robust and efficient solution for optimizing the e-procurement process and adeptly managing the complexities of the supply chain environment. [ABSTRACT FROM AUTHOR]

In recent years, the demand for dry bulk cargo trade has increased, and the scheduling pressure on bulk cargo ports is increasing daily. The most effective and cost-effective way to alleviate the pressure of port scheduling management rapidly is to optimize the overall scheduling of different port resources. Channel vessel scheduling and berth–yard loading and unloading operations are usually regarded as independent operations; however, plans that meet the needs of comprehensive port management can only be effectively developed through the integrated scheduling of multiple resources. Therefore, from the perspective of overall port management and operation, considering practical factors in scheduling, this article construct a multiobjective optimization model for the overall scheduling of restricted channels, berths, and yards in bulk cargo ports. Based on actual allocation principles, corresponding allocation algorithms for storage yards and stacker–reclaimer machines are designed, and these algorithms are combined with a multiobjective genetic algorithm to construct the NSGA-II-DPGR algorithm for model calculation. Results show that the proposed model and algorithm can provide vessel scheduling and cargo loading and unloading plans. This article experimentally verify the rationality of the results, the superiority of the strategy compared to the traditional first-come, first-serve strategy, and the feasibility of combining the proposed model and algorithm with onshore scheduling in the future. [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]

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]

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]

Transportation network vulnerability envelope (TNVE), constituted by the upper and lower bounds of network performance among all possible disruption scenarios, has recently been proposed as a systematic tool to characterize the impact of simultaneous disruptions of multiple links in a transportation network. Both pessimistic and optimistic cases and the possible range of disruption consequences can be revealed by the TNVE, which can be modeled as a unified optimization problem without the need to enumerate and evaluate all possible disruption scenarios. Specifically, the TNVE problem can be formulated as a binary integer bi-level program (BLP), in which the upper-level problem maximizes/minimizes the remaining network performance under a given number of disrupted links, and the lower-level problem adopts the shortest path problem to check the post-disruption connectivity of each origin–destination (O-D) pair while circumventing the cumbersome path enumeration or path set pre-generation. However, the binary integer BLP is computationally intractable, which hinders the applications of TNVE in practice. This study aims to develop an efficient method based on the random key genetic algorithm (RKGA) for determining the TNVE under simultaneous multi-link disruptions. The main features and benefits of the proposed method include: (a) it simultaneously solves the upper- and lower-bound problems at the same time while guaranteeing the feasibility of all solutions in the solution procedure; (b) it improves the computational efficiency to ensure the applicability to real transportation networks; and (c) it can provide a variety of alternative solutions in addition to the single optimal one, which facilitates the derivation of TNVE buffer and identification of sub-important links. These benefits make the proposed method efficient and effective for solving the TNVE problem. The applicability of the proposed method is demonstrated with small and medium-sized networks, as well as a large-scale real road network. Numerical experiments are conducted to illustrate the usage of TNVE for vulnerability analysis of transportation networks. [ABSTRACT FROM AUTHOR]

Optimizing the order-fulfillment process is crucial to the logistics industry. Replacing experience-based judgment with intelligent methods in the packing process not only improves work efficiency but also eliminates resource wastage. This study focuses on the three-dimensional multiple bin-size bin packing problem with compatible categories (3D-MBSBPPCC) and different size of the boxes. An algorithm is developed to achieve the best packing pattern while minimizing the number of boxes required and the total unused space in the boxes. In the algorithm, we first use the first-fit decreasing algorithm to find an initial solution. Next, the carton configuration algorithm is applied to perform packing and evaluate the solution. Subsequently, a simulated annealing (SA)-based algorithm is employed to improve the solution by moving the products among the subsets of the solution. Finally, hybrid metaheuristics based on both SA and the genetic algorithm, are proposed to improve the solution quality. We tested the proposed packing algorithms on real-world scenarios. The experimental results demonstrate that the proposed algorithms are effective in solving 3D-MBSBPPCC. [ABSTRACT FROM AUTHOR]

This article offers a Fuzzy Cognitive Map (FCM) - based method of empirical modeling which can be considered as an alternative to multivariate regression analysis for the input–output relationship extraction from experimental data. Vertices of the FCM graph are interpreted as input–output variables of the empirical model, and the weights of arcs are unknown parameters that are estimated in two stages. The first stage consists of the arcs weights estimating based on the proximity of the columns' values in the input–output observation data table. At the second stage, the observation table is used for the offline weights adjustment using the least squares method and the optimization procedure by genetic algorithm. The method is illustrated by the example of the World Bank data testing the relations between Gross Domestic Product per Capita vs demographic, educational, economic, technological and other factors. The novelty of the method in comparison to the Soft Regression technique lies in the utilization of the FCM arcs weights adjustment according to the least squares criterion. [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]

In this study, a two-phase algorithm has been developed to address the single-product supplier selection and order allocation problem (SP-SSOAP). In the first phase, a novel demand allocation algorithm is developed using a recursive demand equation. Subsequently, an adaptive genetic algorithm is employed in the second phase, with additional constraints for capacity and minimum order quantity, to determine the best lot-sizing plan for each supplier. Experimental results demonstrate that, on average, the proposed heuristic achieves an objective value within 1% of the optimal solution for small-sized instances and outperforms the previous heristic solution by an average of 12% in terms of objective value for larger problems. Additionally, the numerical results emphasize that the proposed method performs better when input parameters such as price, ordering cost, and holding cost exhibit lower fluctuations. This suggests that managers should focus on improving supplier relationships and coordination strategies to reduce variability in price, order cost, and holding cost. [ABSTRACT FROM AUTHOR]

A new hybrid system is proposed for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a dynamic fitness function to obtain the smallest circuit complexity and highest accuracy on unseen data, ensuring that the proposed technique is generalizable and robust. At the same time, it is minimized the complexity of the generated circuits in terms of the number of entangling operators by penalizing their appearance and number of gates. The size of the images is reduced by using two dimensionality reduction approaches: principal component analysis (PCA), which is encoded within the individual and genetically optimized by the system, and a small convolutional autoencoder (CAE). These two methods are compared with one another and with a classical nonlinear approach to understand their behaviors and to ensure that the classification ability is due to the quantum circuit and not the preprocessing technique used for dimensionality reduction. • AutoQML: an automatic tool for generating and training quantum classifiers. • Complexity and accuracy optimization using multi-objective genetic algorithms. • Application of quantum systems in high-dimensional data such as grayscale images. • Comparison among dimensionality reduction methods for QML and a classical system. • Improved classification by coding the dimensionality reduction method in individual. [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]

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]

Pneumonia is a lung illness that may result from a variety of various viral diseases and may be lethal. It might be difficult to diagnose and treat pneumonia on chest X-ray pictures because pneumonia and other lung illnesses are closely connected. As a result, the present methods for forecasting pneumonia cannot achieve meaningful levels of accuracy. This study aids in identifying diseases from chest X-ray images that don't exhibit the same symptoms as pneumonia. Five stages will be included in the projected model: (a) pre-processing, (b) segmentation, (c) feature extraction, (d) optimal feature selection, and (e) detection. The gathered image is initially pre-processed using Dynamic Histogram Equalization (DHE), followed in this case by median filtering. At the segmentation phase, by utilizing the Enhanced Watershed Segmentation, the ROI is separated from the background region of the pre-processed image. Additionally, in the feature extraction step, features based on Local Gradient Increasing Pattern (LGIP), Harmonic Local Gradient Pattern (hLGP), Improved Local Ternary Pattern (ILTP), and Improved Local Gradient Pattern (ILGP) will be derived from the isolated ROI areas. Using the proposed AHGOA (Archimedes-assisted Henry Gas Optimization Algorithm) model, the best characteristics from these retrieved features were selected. This is the best option for avoiding the dimensionality curse. The selected EC + AHGOA method obtain good accuracy (∼0.95) for tuning percentage 70 in pneumonia diagnosis from Chest X-ray images than some other previous techniques includes EC + AOA (∼0.92), EC + HGSO (∼0.93), EC + HGS (∼0.88), EC + PRO (∼0.90), and EC + BES (∼0.89). [ABSTRACT FROM AUTHOR]

Optimization of subsurface hydrocarbon production holds paramount importance for decision-makers as it determines crucial development strategies such as optimal well placement and well control parameters (e.g. injection/production rates of injectors and producers). Despite the availability of numerous established optimization methods in this field, traditional reservoir production optimization methods face challenges in simultaneously addressing multiple development objectives and coordinating the interaction of well control parameters in different control steps. In this work, we construct a hybrid artificial intelligence method to jointly optimize well placement and well control parameters, taking into account two development objectives and dynamic optimization. It consists of two stages. First, a reservoir potential map is generated with the production potential formula that considers reservoir pressure, remaining oil saturation and other reservoir properties (permeability, hydrocarbon column height) etc. The reservoir potential map provides guidance for placing well in medium to high potential areas and engineering constraints for the optimization process. Then, a hybrid artificial intelligence method that couples deep learning method (Long and Short Term Memory (LSTM)) and multi-objective optimization algorithm (Non-dominated Sorting Genetic Algorithm II (NSGA-II)) is established to seek a compromise between the two objectives in water-flooding processes. The LSTM neural network is trained as the surrogate model to replace the high-fidelity simulator to achieve high efficiency of overall optimization workflow. The NSGA-II algorithm is employed for handling the joint optimization problem of well placement and well control parameters by maximizing the cumulative oil production and minimizing the water cut. The performance of the proposed method is tested on one benchmark function and two reservoir models. On the 2D synthetic reservoir model the optimized scheme leads to a notable increase of 3 × 104 m3 in cumulative oil production, accompanied by 17 % reduction in water cut when contrasted with the base scheme. Similarly, within the 3D reservoir model, the optimized scheme results in a substantial enhancement, boosting cumulative oil production by 14 × 104 m3 and reducing water cut by 20% compared to the base scheme. Moreover, the proposed method surpasses alternative multi-objective optimization (MOO) algorithms by demonstrating 82% and 95% reduction in optimization time, respectively. The results demonstrate that this method can provide the optimal water-flooding strategies under the premise of different development objectives. The Pareto front (or optimal solutions) generated by the hybrid method offers a variety of diverse water-flooding strategies to assist subsurface engineers in making informed decisions. [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]

The move to predictive and prescriptive maintenance underlines the need for cost-effective methods to monitoring degradation and predicting failures. It is in hydroelectric power plants (HPPs) where the most powerful and unique techniques of this evolution were observed. The final premise of this transition is the need to improve the decision-making process for planning activities in the Prescriptive Maintenance Planning (MP). This encourages the use of fuzzy set theory in process management. Although there are many studies on MP in the literature, these studies do not fully reflect the real life as they ignore potential schedule delays and the indirect impact of sustainable strategy on revenues. Therefore, in the first stage of this study, it is aimed to weight the maintenance types (MTs) on a plant basis for the generator, which is the most critical system of the pumped storage HPP, and in the second stage, it is aimed to create MPs that minimize delays due to system shutdowns with weighted MTs. For this purpose, in the first stage, the MTs for the HPP were weighted by AHP-TOPSIS combination augmented with Pythagorean fuzzy numbers, and in the second stage, a Genetic Algorithm model was designed for the problem of coordinating maintenance durations and deadlines in addition to these weights. Averaged over 50 iterations, the average number of delayed MPs is 142.55, indicating that the algorithm is successful in reducing delayed MPs, while the average weighted delay value is 172.85, indicating that more work is needed in terms of weighted delay. [ABSTRACT FROM AUTHOR]

Pseudo-random number generators (PRNGs) play an important role to ensure the security and confidentiality of image cryptographic algorithms. Their primary function is to generate a sequence of numbers that possesses unpredictability and randomness, which is crucial for the algorithms to work effectively and provide the desired level of security. However, traditional PRNGs frequently encounter limitations like insufficient randomness, predictability, and vulnerability to cryptanalysis attacks. To overcome these limitations, we propose a novel method namely an elliptic curve genetic algorithm (ECGA) for the construction of an image-dependent pseudo-random number generator (IDPRNG) that merges elliptic curves (ECs) and a genetic algorithm (GA). The ECGA consists of two primary stages. First, we generate an EC-based initial sequence of random numbers using pixels of a plain-image and parameters of an EC that depart from traditional methods of population initialization. In our proposed approach, the image itself serves as the seed for the initial population in the genetic algorithm optimization, taking into account the image-dependent nature of cryptographic applications. This allows the PRNG to adapt its behavior to the unique characteristics of the input image, leading to enhanced security and improved resistance against differential attacks. Furthermore, the use of a good initial population reduces the number of generations required by a genetic algorithm which results in decreased computational cost. In the second stage, we use well-known operations of a genetic algorithm to optimize the generated sequence by maximizing a multi-variable fitness function that is based on both the information entropy and the period of the PRNG. By combining elliptic curves and genetic algorithms, we enhance the randomness and security of the ECGA. To evaluate the effectiveness and security of our generator, we conducted comprehensive experiments using various benchmark images and applied several standard tests, including the National Institute of Standards and Technology (NIST) test suite. We then compared the results with the state-of-the-art PRNGs. The experimental results demonstrate that the ECGA outperforms the state-of-the-art PRNGs in terms of uniformity, randomness, and cryptographic strength. [ABSTRACT FROM AUTHOR]

Population-based optimization algorithms are popular tools for optimization, influenced by two interactive factors: landscape bias, which guides the population toward better objective function values, and structural bias, which directs the population to a specific location in the search domain. Structural bias can impede the exploration of algorithms, compelling the population to revisit a particular area of the search domain, which leads to increased computing costs without yielding any new information. In this study, we present a comprehensive survey of structural bias. Identifying this bias in a general algorithm is challenging. We propose the 'Generalized Signature Test' as a methodology to detect and analyze the structural bias in population-based optimization algorithms. Numerical simulations are performed using six well-known metaheuristic algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, and Harris Hawks Optimization. Experimental results suggest that Differential Evolution displays the least bias toward the center of the search domain, whereas the Harris Hawks Optimization exhibits a significant axial-diagonal bias toward the origin. Our findings indicate that the proposed method is both efficient and straightforward for assessing algorithmic structural bias. The Generalized Signature Test provides an in-depth analysis and understanding of biases, tailor-made for thorough bias behavior investigations. The Generalized Signature Test can identify bias in established algorithms and evaluate bias in emerging algorithms at their early development stages. [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]

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]

The optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Robot Gripper Design Optimization Problem is a well-known multi-objective optimization problem explored by various approaches in previous studies over the last few decades. The continuous parameter z plays an important role in determining a robot gripper's minimum and maximum gripping forces. However, previous studies have utilized a simple loop approach in which the value of z is iteratively increased by a constant value from 0 to 50 to determine the gripping forces of a solution. This approach limits the solution space by treating the continuous z parameter as a discrete variable. To overcome this limitation, we propose a nested optimization approach in which a single-objective optimizer is embedded within the fitness function of the main optimizer to determine the optimal value of z. The proposed approach is evaluated using a single-optimizer Simulated Annealing (SA) nested within multi-objective optimizers such as Multi-Objective Gray Wolf Optimizer (MOGWO), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Artificial Algae Algorithm (MOAAA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II). Experimental results demonstrate that our approach significantly outperforms previous studies and achieves the best-known performance. The findings of the study indicate that this method can present a novel framework for researchers in tackling design optimization challenges, encouraging a reevaluation and possible improvement of conventional techniques. [ABSTRACT FROM AUTHOR]

Several surveys in the Indoor Positioning System (IPS) use the Radio Signal Strength Indicator (RSSI) to locate target objects. However, RSSI suffers significantly from the multipath phenomenon and other environmental factors such as the absence of the field of view, reflective materials, excessive obstacles, and a high density of objects. To overcome such problems, we propose three new IPS approaches using Radio Frequency Identification (RFID) technology and the collaborative use of Sliding Window, Hierarchical Classification, Random Forest Regressor, and Genetic Algorithm. We tested the new approaches on three datasets obtained in different environments and compared them with two state-of-the-art IPS approaches (Landmarc and SVR-Landmarc). One of the main results got was a precision of 4.82 cm in an environment with 7,000 target tags, reducing by up to 92.91% the average error w.r.t. other IPS approaches presented in the literature, including more complex indoor scenarios. • The problem of interference in radiofrequency waves in RFID systems is addressed. • The sliding window to smooth out interference from RFID readings is introduced. • Three new RFID indoor positioning systems are proposed. • An RFID dataset for indoor location is made available. • A complete experimental analysis and comprehensive comparison are provided. [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]

Computed tomography (CT) is a non-invasive testing technique to generate the internal image of any object. It has various applications belonging to medical imaging and other engineering applications. The present article proposes a novel multiagent genetic algorithm based on hill-climbing for limited-view computed tomography (MAGAH-CT). The MAGAH combines a multi-agent system based on a genetic algorithm (MAGA) and a hill-climbing approach. The MAGAH uses multiple operators to refine the agent population during evolution. These operators are neighborhood-based competition, hybrid crossover, adaptive mutation, and self-learning. MAGAH-CT explores better solutions in neighborhood-based competition and cooperation operators. The self-learning operator learns through the agent's population generated by adaptive mutation and hybrid crossover operator, ensuring the results' continuity. The proposed algorithm has been tested on a Shepp-Logan head phantom with limited view projection data. The algorithm can accurately reconstruct the low-noise CT image using limited data constraints. Experimental results reveal that the presented MAGAH-CT algorithm is efficient and computationally reasonable. The presented algorithm is suitable for low-noise CT reconstruction and out-performs other limited-view CT reconstruction algorithms. [ABSTRACT FROM AUTHOR]

The amplification of the 16S ribosomal RNA gene through the polymerase chain reaction (PCR) is the main approach to profile bacterial communities. This gene is a widely used marker with a composition that allows the identification of microorganisms at the genus or species levels. The correct performance of a PCR assay depends on the properties of the chosen set of primers that match with the target 16S sequences, allowing the amplification by a DNA polymerase. For this reason, optimizing the design of primers attending to such multiple properties is crucial to ensure the specificity and robustness of this process. However, only one multiobjective proposal exists that addresses the optimization of primer design targeted to the 16S gene amplification. Herein, we propose a novel approach for multiobjective primer optimization based on a mutation-based genetic algorithm with three new problem-aware mutation operators, each aimed at covering one of the three objectives that compose the problem (efficiency, coverage, and variability). The proposed algorithm has been tested on 5 real datasets of bacterial 16S gene sequences. The results have been evaluated using 4 quality metrics, showing that our approach achieves statistically significant improvements with regard to the reference multiobjective approach in the field. • Novel multiobjective approach for PCR primer optimization targeting 16S genes. • Design and implementation of three new problem-aware mutation operators. • Comprehensive experimental study with 5 real datasets and 4 quality metrics. • Comparison with the only multiobjective proposal previously published in this field. • Statistically significant improvements regarding the state of the art. [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]

• It investigates comprehensive joint decision-making in a dyadic supply chain. • It develops a Stackelberg game model to elaborate the joint decision-making. • It develops a nested genetic algorithm to solve the complex game model. • It demonstrates the applicability of the model and the robustness of the algorithm. • It obtains some important managerial implications. In practice, manufacturers and their independent retailers in dyadic supply chains jointly make decisions by capitalizing on decision interactions while respecting the carbon emission tax and subsidy determined by local governments. Though studies have been published to address the joint decision-making, they involve only a very few of the important supply chain decisions due to the problem complexities. In this study, we, therefore, investigate a comprehensive joint decision-making of a manufacturer and his independent retailer considering both carbon emission tax and subsidy offered by the local government. The decisions in our study include i) the manufacturer's technology selection, production quantities, and wholesale price and ii) the retailer's retail price. Per the decision interactions, we analyze the decision-making problem as a Stackelberg game. The game model developed, by nature, is a bilevel 0–1 mixed nonlinear programming, and cannot be solved analytically. Considering its complexities, we further develop a nested genetic algorithm (NGA) to solve the model. Numerical examples demonstrate the applicability of the Stackelberg game model in facilitating supply chain members to jointly make decisions and the robustness of the NGA. With sensitivity analysis, we shed light on several important managerial implications, such as, manufacturers need to identify "optimal" ranges of emissions released from producing a unit of green (or dirty) product to obtain higher profits; manufacturers need to control well their production processes so that emissions released from producing a unit of product from a green (or dirty) technology fall in "optimal" ranges contributing to higher profits. [ABSTRACT FROM AUTHOR]