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Virtual machine placement (VMP) has a significant importance with respect to resource utilization in cloud data centers. Indeed, the optimized management of machine placement usually results in a significant reduction in energy consumption. VMP is a bin packing problem generalization, which is a well known hard combinatorial optimization problem. Besides being NP-hard, VMP is characterized by conflicting objectives and a noisy search space. Meta-heuristics, such as genetic algorithms, particle swarm optimization (PSO), cuckoo search (CS), tabu search and simulated annealing (SA) have been shown to be effective for this category of problems. This paper reports a performance comparison between SA, CS and PSO meta-heuristics to solve the VMP problem. In contrast to reported research work in this area, we study the performance behavior of these three meta-heuristics with respect to, not only the quality of solutions, but also the quality of the explored solution sub-space, in addition to the convergence speed towards reported solutions and the speed with which each meta-heuristic evolves towards the best reported optimized solution. Extensive simulations on randomly generated tests with sizes varying between 200 and 1000 virtual machine demands show that PSO achieves the best performance behavior with respect to all criteria. Moreover, for all tests, PSO produces a reduction of as much as 17% of the number of physical machines, 15% of the energy cost and 21% of the resource utilization of physical machines. [ABSTRACT FROM AUTHOR]

In multi-mass systems, torsional vibration is a common and annoying phenomenon. Effective vibration suppression and robustness to wide-range parameter variations are essential for a sound motion system. However, most control methods focus on the primary resonance mode, and the high-order resonance modes are not actively treated in the control design, resulting in the control bandwidth not being high enough and limiting the control performance. This paper proposes a novel two-stage design scheme to realize a wideband control to improve control performance. First, a hybrid uncertainty model is tailored for multi-mass systems, which uses an equivalent and uncertain spring constant to describe the variation of the primary mode and a dynamic uncertainty to cover the other resonance modes. This hybrid model strikes a better balance between the model conservatism and the feasibility of a less conservative design. Then, the passivity of the parameter uncertainty is utilized to conduct a phase compensation on the nominal system. After the phase compensation, all uncertainties are converted into norm-bounded ones, and the robust performance design is carried out. This method is applied to vehicle drivetrain benches, and its superiority is validated through simulation comparisons and experiments on two typical types of drivetrain benches. • Hybrid model for multi-mass systems characterizes primary resonance and other modes. • Two-stage design method with phase-shaping achieves high performance robust control. • The method is applied to drivetrain benches, outperforming the conventional methods. [ABSTRACT FROM AUTHOR]

Lagrangian heuristics for discrete optimization work by modifying Lagrangian relaxed solutions into feasible solutions to an original problem. They are designed to identify feasible, and hopefully also near-optimal, solutions and have proven to be highly successful in many applications. Based on a primal-dual global optimality condition for non-convex optimization problems, we develop a meta-heuristic extension of the Lagrangian heuristic framework. The optimality condition characterizes (near-)optimal solutions in terms of near-optimality and near-complementarity measures for Lagrangian relaxed solutions. The meta-heuristic extension amounts to constructing a weighted combination of these measures, thus creating a parametric auxiliary objective function, which is a close relative to a Lagrangian function, and embedding a Lagrangian heuristic in a search procedure in the space of the weight parameters. We illustrate and make a first assessment of this meta-heuristic extension by applying it to the generalized assignment and set covering problems. Our computational experience show that the meta-heuristic extension of a standard Lagrangian heuristic can significantly improve upon solution quality.  [ABSTRACT FROM AUTHOR]

Fraud detection in banking systems is crucial for financial stability, customer protection, reputation management, and regulatory compliance. Machine Learning (ML) is vital in improving data analysis, real-time fraud detection, and developing fraud techniques by learning from data and adjusting detection strategies accordingly. Feature Selection (FS) is essential for enhancing fraud detection through ML to achieve optimal model accuracy. This is because it helps to eliminate the negative impact of redundant and irrelevant attributes. To enhance the accuracy of the given dataset, the researchers utilized multiple methods to determine the most fitting features. However, it is important to note that when implementing these methods on datasets with larger feature sizes, they may encounter issues with local optimality. Despite this, the researchers continue to work on improving the effectiveness of these methods. This study presents an effective methodology based on the Brown-Bear Optimization (BBO) algorithm to enhance the capacity to accurately identify financial CCF transactions by recognizing pertinent features. BBO has balanced capabilities to reduce dimensionality while enhancing classification accuracy. It is improved by adjusting the positions randomly to enhance exploration and exploitation capabilities, and then it is cloned into a binary variant named Binary BBOA (BBBOA). The Support Vector Machine (SVM), k-nearest Neighbor (k -NN), and Xgb-tree are the ML classifiers used with the suggested methodology. On the Australian credit dataset, the proposed methodology is compared with the basic BBOA and ten current optimizers, such as Binary African Vultures Optimization (BAVO), Binary Salp Swarm Algorithm (BSSA), Binary Atom Search Optimization (BASO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Sailfish Optimizer (BSFO). Regarding Wilcoxon's rank-sum test (α = 0. 05), the superiority and effective consequence of the presented methodology are clear on the utilized dataset and got an accuracy of classification up to 91% in the utilized dataset combined with an attribute reduction length down to 67%. The proposed methodology is further validated using 10 benchmark datasets and outperformed its competitors in most utilized datasets regarding different performance measures. In the end, the proposed methodology is further validated using ten benchmark datasets from the UCI repository. It outperformed its competitors in most of the utilized datasets regarding different performance measures. [ABSTRACT FROM AUTHOR]

In this study, the design of a sustainable closed-loop supply chain network for agricultural products with the goals of minimizing the cost and emission of greenhouse gases and maximizing the response to customer demand, and creating justice-based job opportunities, simultaneously, are aimed. The intended chain is based on the fruit supply chain study, which includes fresh fruits, concentrate, and vermicompost fertilizer. A mixed-integer linear programming (MILP) model has been developed to achieve the triple bottom line. Due to the nature of the NP-hard problem, the proposed model is solved using metaheuristic approaches consisting of two renowned algorithms, NSGA-II and NRGA, and a relatively new algorithm called NSGA-III. It is worth noting that the parameters of the algorithms are adjusted to achieve the best performance in small, medium, and large-size problems exerting the Taguchi method. After comparing the results of the three algorithms based on the well-known criteria, NRGA is introduced as the superior algorithm. Ultimately, the results of sensitivity analysis indicate that appending the possibility of using vermicompost in gardens and considering several vehicles in the proposed sustainable supply chain boosts the values of economic and environmental objective functions to about 6.4 and 8.2%, respectively. [ABSTRACT FROM AUTHOR]

Polycystic Ovary Syndrome (PCOS) is a hormonal state that affects women within the reproductive age group. Early and accurate diagnosis of medical conditions is crucial for the successful implementation of medication and prevention of significant health problems. In recent times, there has been an increasing amount of evidence suggesting that machine learning (ML) methods have significant potential in facilitating the diagnosis and classification of PCOS. The objective of this research is to evaluate the viability of using several machine learning algorithms, such as Random Forests, Decision Trees, Artificial Neural Networks, and XGBoost, for the purpose of classifying PCOS using clinical and laboratory features. Furthermore, the use of the Sine Cosine Algorithm was implemented for the purpose of selecting features in order to improve the accuracy and efficiency of the model. A sample of 500 individuals was selected for the purpose of training and assessing the models, with half of the participants (250) diagnosed with PCOS and the other half not exhibiting the disease. The findings suggest that the classification algorithms had strong performance in classifying PCOS, with XGBoost having the best level of performance. In addition, the use of the Sine Cosine Algorithm resulted in enhanced model precision and effectiveness via the identification of the most pertinent features for the classification objective. The results of this study emphasise the significant capabilities of machine learning methods, namely XGBoost and the Sine Cosine Algorithm, in aiding the diagnosis and classification of PCOS in a medical setting. Additional investigation is required in order to substantiate the models and prove their clinical significance in the provision of patient care. [ABSTRACT FROM AUTHOR]

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO‐SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO‐SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state‐of‐the‐art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real‐world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta‐heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO‐SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non‐gradient‐based optimizers. [ABSTRACT FROM AUTHOR]

Introduction: Fuel cell technology is a harbinger of the future for generating electricity due to their high efficiency and low emissions achieved through the direct conversion of chemical energy into electrical energy without combustion. Methods: To optimize the design and performance, a fuel cell model is essential to predict its behaviour in different conditions. This technical note presents a novel physics-based approach, the Young's Double-slit Experiment Optimizer (YDEO), for identifying parameters in Proton Exchange Membrane Fuel Cells. A performance metric is established by formulating an objective function that relies on the summation of squared errors between experimental and estimated values. Results and discussion: The effectiveness of this approach is evaluated through the analysis of four benchmark test cases: Horizon 500 W, BCS500 W, NedstackPS6, and 250 W. The corresponding objective function values for these test cases are 0.011243, 2.065557, 0.011698, and 5.250849, respectively. The simulation results demonstrate the efficacy of the proposed YDEO algorithm when compared with other existing popular and contemporary algorithms in the literature. [ABSTRACT FROM AUTHOR]

A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO’s behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO. [ABSTRACT FROM AUTHOR]

The flow shop scheduling problem is important for the manufacturing industry. Effective flow shop scheduling can bring great benefits to the industry. However, there are few types of research on Distributed Hybrid Flow Shop Problems (DHFSP) by learning assisted meta-heuristics. This work addresses a DHFSP with minimizing the maximum completion time (Makespan). First, a mathematical model is developed for the concerned DHFSP. Second, four Q-learning-assisted meta-heuristics, e.g. genetic algorithm (GA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), and differential evolution (DE), are proposed. According to the nature of DHFSP, six local search operations are designed for finding high-quality solutions in local space. Instead of random selection, Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations. Finally, based on 60 cases, comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms. The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy. To verify the competitiveness of the Q-learning assistedmeta-heuristics, they are compared with the improved iterated greedy algorithm (IIG), which is also for solving DHFSP. The Friedman test is executed on the results by five algorithms. It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG, and the Q-learning-assisted PSO shows the best competitiveness. [ABSTRACT FROM AUTHOR]

Recently, solving complex real-world challenges has become a significant and vital task, many of these challenges involve combinatorial issues where optimal solutions are desired. Traditional optimization strategies have proven to be efficient for small-scale problems. However, when it comes to larger problems, such as those in the finance or business domains, a meta-heuristic search algorithm, like the Spotted Hyena optimization (SHO), is implemented. However, the SHO algorithm faces a challenge in selecting the optimal cluster head, resulting in low efficiency. To address this issue, a new bio-inspired optimization strategy called a novel Fuzzy Based Spotted Hyena Optimizer (FSHO) is proposed. This novel algorithm aims to solve the problem of optimum clustering and provides a solution to real-world engineering challenges. In addition, the Spotted Hyena algorithm is combined with Fuzzy C-Means to enhance its exploration capability, making it well-suited for optimization tasks. The implementation of this strategy is carried out using MATLAB, and its performance is evaluated using 29 benchmark functions, as well as by addressing various complex and computationally expensive engineering issues. Comparing the cost analysis of existing techniques such as FFA, AVOA, MGO, and GTO, the proposed FSHO achieves significantly better results with a cost reduction of 1.2%, as opposed to 5%, 3%, 2.7%, and 1.5% achieved by the other techniques, respectively. This method not only enhances performance but also provides cost-effective analysis with a low convergence rate. [ABSTRACT FROM AUTHOR]

Fraud detection in banking systems is crucial for financial stability, customer protection, reputation management, and regulatory compliance. Machine Learning (ML) is vital in improving data analysis, real-time fraud detection, and developing fraud techniques by learning from data and adjusting detection strategies accordingly. Feature Selection (FS) is essential for enhancing fraud detection through ML to achieve optimal model accuracy. This is because it helps to eliminate the negative impact of redundant and irrelevant attributes. To enhance the accuracy of the given dataset, the researchers utilized multiple methods to determine the most fitting features. However, it is important to note that when implementing these methods on datasets with larger feature sizes, they may encounter issues with local optimality. Despite this, the researchers continue to work on improving the effectiveness of these methods. This study presents an effective methodology based on the Brown-Bear Optimization (BBO) algorithm to enhance the capacity to accurately identify financial CCF transactions by recognizing pertinent features. BBO has balanced capabilities to reduce dimensionality while enhancing classification accuracy. It is improved by adjusting the positions randomly to enhance exploration and exploitation capabilities, and then it is cloned into a binary variant named Binary BBOA (BBBOA). The Support Vector Machine (SVM), k-nearest Neighbor (k-NN), and Xgb-tree are the ML classifiers used with the suggested methodology. On the Australian credit dataset, the proposed methodology is compared with the basic BBOA and ten current optimizers, such as Binary African Vultures Optimization (BAVO), Binary Salp Swarm Algorithm (BSSA), Binary Atom Search Optimization (BASO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Sailfish Optimizer (BSFO). Regarding Wilcoxon’s rank-sum test (α=0.05), the superiority and effective consequence of the presented methodology are clear on the utilized dataset and got an accuracy of classification up to 91% in the utilized dataset combined with an attribute reduction length down to 67%. The proposed methodology is further validated using 10 benchmark datasets and outperformed its competitors in most utilized datasets regarding different performance measures. In the end, the proposed methodology is further validated using ten benchmark datasets from the UCI repository. It outperformed its competitors in most of the utilized datasets regarding different performance measures.

Since late 2019, coronavirus disease 2019 (COVID-19) has been spreading globally, presenting a significant threat to human lives and health, and exerting a profound impact on worldwide economic development. Due to the highly contagious nature of COVID-19, precise and prompt diagnosis has become paramount. The effective and rapid identification of COVID-19 through computed tomography (CT) images has thus garnered substantial interest, prompting scientists to propose various segmentation methods aimed at improving the diagnostic accuracy of CT images. Drawing from these foundations, the study introduces an innovative multilevel threshold segmentation method known as the Reinforcement Learning-based Enhanced Sand Cat algorithm (QLSCSO). QLSCSO represents a novel optimization algorithm distinguished by its remarkable convergence accuracy and the capacity to escape local optima. The introduction of this optimizer incorporates reinforcement learning methodologies into the population iteration process of heuristic techniques. In the algorithm’s update phase, a hybrid model and three distinct mutation strategies are employed to enhance its capability to overcome local optima. Consequently, the developed QLSCSO method produces high-quality segmentation results while reducing vulnerability to segmentation process stagnation. To establish the effectiveness of the proposed method, comparative analyses are initiated between QLSCSO and other advanced meta-algorithms using the IEEE CEC 2022 benchmark functions. Furthermore, QLSCSO undergoes experimental evaluations on CT images of COVID-19, including comprehensive comparative assessments with other competing segmentation methods and thorough validation. The results conclusively demonstrate the outstanding performance of the unique segmentation method based on QLSCSO across a range of performance evaluation metrics. Therefore, this approach offers an efficient segmentation procedure for COVID-19 images and even other pathological medical images. [ABSTRACT FROM AUTHOR]

Due to the complexity and the magnitude of optimisation models that appeared in sustainable supply chain management (SSCM), the use of meta-heuristic algorithms as competent solution approaches is being increased in recent years. Although a massive number of publications exist around SSCM, no extant paper explicitly investigates the role of meta-heuristics in the sustainable (forward) supply chain. To fill this gap, a literature review is provided on meta-heuristic algorithms applied in SSCM by analyzing 160 rigorously selected papers published by the end of 2020. Our statistical analysis ascertains a considerable growth in the number of papers in recent years and reveals the contribution of 50 journals in forming the extant literature. The results also show that in the current literature the use of hybrid meta-heuristics is overtaking pure meta-heuristics, the genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most-used single- and multi-objective algorithms, the aspects of sustainability are mostly addressed in connection with product distribution and routing of vehicles as pivotal operations in supply chain management, and last but not least, the economic-environmental category of sustainability has been further noticed by the scholars. Finally, a detailed discussion of findings and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Today, the algorithm selection paradigm has become one of the promising approaches in the field of optimization problems. Its main goal is to solve each case of an optimization problem with the most accurate algorithm using machine learning techniques. This paper treats the issue of the algorithm selection for the Single Machine Scheduling Problem with Early/Tardy jobs by adapting three meta-heuristics from the state-of-the-art, namely genetic algorithm, particle swarm optimization, and tabu search. In the proposed framework, we combine the running time and the cost function to get a new performance criterion. A large set composed of 98000 instances of the problem is generated with 12 features characterizing each instance. We carry a statistical comparison of the implemented meta-heuristics, and we evaluate 10 classifiers. It can be deduced that the Dagging algorithm combined with the Random Forest is the most likely to be the best classifier, which achieves 88.44% of the maximum accuracy.

Abstract As a preprocessing for machine learning and data mining, Feature Selection plays an important role. Feature selection aims to streamline high-dimensional data by eliminating irrelevant and redundant features, which reduces the potential curse of dimensionality of a given large dataset. When working with datasets containing many features, algorithms that aim to identify the most valuable features to improve dataset accuracy may encounter difficulties because of local optima. Many studies have been conducted to solve this problem. One of the solutions is to use meta-heuristic techniques. This paper presents a combination of the Differential evolution and the sailfish optimizer algorithms (DESFO) to tackle the feature selection problem. To assess the effectiveness of the proposed algorithm, a comparison between Differential Evolution, sailfish optimizer, and nine other modern algorithms, including different optimization algorithms, is presented. The evaluation used Random forest and key nearest neighbors as quality measures. The experimental results show that the proposed algorithm is a superior algorithm compared to others. It significantly impacts high classification accuracy, achieving 85.7% with the Random Forest classifier and 100% with the Key Nearest Neighbors classifier across 14 multi-scale benchmarks. According to fitness values, it gained 71% with the Random forest and 85.7% with the Key Nearest Neighbors classifiers.

Cloud computing has gradually become one of the most popular platforms for executing scientific applications due to its elastic and on-demand resource provisional capabilities. But, how to effectively schedule a set of parallel workflows to minimize the makespan under their individual budget constraints remains a critical problem. This work proposes a Clustering-assisted Gradient-Based Optimizer (C-GBO) to improve the performance for scheduling workflows in cloud environments. First, it designs a novel individual encoding mechanism including task-VM mapping and task-priority sub-strings to further optimize the makespan by updating both sub-strings simultaneously, especially each element representing task execution order in a task-priority sub-string can take any values within the pre-specified range but not subject to control dependencies among tasks. Second, to address the original GBO's easiness of falling into local optima brought by its only one best guiding solution, it divides individuals into different groups as their position information by the K-means algorithm and selects the best guiding solution for each group with 50% probability from their own clusters locally, such that the search diversity is improved and the cross evolution is reduced. Third, a Gaussian disturbance-based elite enhancement strategy is developed by introducing a Gaussian disturbance operation to a certain number of each elite individual so as to fully exploit these individuals and increase the quality of the global best solution. Our proposed C-GBO algorithm is testified and compared with six peers on datasets with different scales through WorkflowSim. The results demonstrate that our proposed C-GBO achieves the best results in all the involved algorithms, i.e., obtaining the success rate of 100% on all datasets, and performs better in the average makespan that is at most 88.47% shorter than its peers in most cases. [ABSTRACT FROM AUTHOR]

This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig's law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compared it to a variety of mainstream algorithms such as SMA, SKA, DBO, GWO, MVO, HHO, WOA, EWOA, and AVOA. The best result of CGO after Friedman testing was 1.6333/10, and the significance level of all comparison results under Wilcoxon testing was lower than 0.05. The experimental results show that the mean and standard deviation of repeated CGO experiments are better than those of the comparison algorithm. In addition, CGO also achieved excellent results in specific applications of robot path planning and photovoltaic parameter extraction, further verifying its effectiveness and broad application potential in practical engineering problems. [ABSTRACT FROM AUTHOR]

Recent meta-heuristics for optimization issues include particle swarm optimization (PSO) techniques. They take their cues from the coordinated movements of swarms of fish and birds, which exhibit collective behaviors. A sophisticated learning phase, like back-propagation, is needed for artificial neural networks (ANNs), which are thought of as a source of artificial intelligence (AI). This phase allows for the calculation of the error gradient for each neuron, from the final layer to the first. However, some qualities of the objective function are necessary (cost). This inspired us to experiment with meta-heuristics to streamline the training of ANNs to manage complex nonlinear systems. The objective of this research is to apply deep reinforcement learning (DRL) to automatically calculate the PSO algorithm's parameters while also optimizing the supervised learning process of ANN. After a number of case studies, our methodology consistently leads to the coefficients of the ideal ANN. [ABSTRACT FROM AUTHOR]

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic Algorithm (GA), Firefly Algorithm (FA), and Particle Swarm Optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes. [ABSTRACT FROM AUTHOR]

The problem is formulated as a mixed-integer nonlinear programming model, with the objective of minimizing sequence-dependent setups while meeting specified cycle time and station count constraints. A tabu search algorithm is proposed to solve the resulting mathematical model, and its performance is assessed through numerical experiments. The developed numerical examples demonstrate significant improvements in setup time reduction. Notably, a substantial enhancement—a 91% reduction in setup time—is observed in moderate-sized problems (n = 50) characterized by high precedence density (OS = 0.6). Subsequently, the developed tabu search algorithm is applied to address the assembly line balancing problem encountered by a leading air conditioner manufacturer. Numerical experiments indicate that the algorithm yields an approximate 10% reduction in both product and operator-oriented setups, showing its effectiveness in enhancing assembly line efficiency. Future research could explore how the developed approach to be applied in various industrial settings including multi-model or mixed-model assembly lines. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and their specialized variants known as unmanned combat aerial vehicles (UCAVs) have triggered a profound change in the well-known military concepts and researchers from different disciplines tried to solve challenging problems of the mentioned vehicles. Path planning is one of these challenging problems about the UAV or UCAV systems and should be solved carefully by considering some optimization requirements defined for the enemy threats, fuel or battery usage, kinematic limitations on the turning and climbing angles in order to further improving the task success and safety of autonomous flight. Immune plasma algorithm (IP algorithm or IPA) modeling the details of a medical method gained popularity with the COVID-19 pandemic has been introduced recently and showed promising performance on solving a set of engineering problems. However, IPA requires setting the control parameters appropriately for maintaining a balance between the exploration and exploitation characteristics and does not design the particular treatment and hospitalization procedures by taking into account the implementation simplicity. In this study, IP algorithm was supported with a newly designed and realistic hospitalization mechanism that manages when an infected population member enters and discharges from the hospital. Moreover, the existing treatment schema of the algorithm was changed completely for improving the efficiency of the plasma transfer operations and removing the necessity of IPA specific control parameters and then a novel path planner called hospital IPA (hospIPA) was presented. For investigating the performance of hospIPA on solving path planning problem, a set of detailed experiments was carried out over twenty test cases belonging to both two and three-dimensional battlefield environments. The paths calculated by hospIPA were also compared with the calculated paths of other fourteen meta-heuristic based path planners. Comparative studies proved that the hospitalization mechanism making an exact discrimination between the poor and qualified solutions and modified treatment schema collecting the plasma being transferred by guiding the best solution give a tremendous contribution and allow hospIPA to obtain more safe and robust paths than other meta-heuristics for almost all test cases. [ABSTRACT FROM AUTHOR]

Uniform sampling in the permutation space is very important for solving permutation problems with NP-hard nature. However, due to the complexity of this space, there is no uniform sampling method for it up to now. In this paper, the description of permutation space and a review of uniform sampling in other space are given. After that, the limitation of the random method for uniform sampling is analyzed, and a k-means clustering algorithm with an improved Borda's method is introduced for sampling based on the above analysis. An extended Latin matrix is defined, and a sampling method based on this matrix that can only solve for a fixed number of sampling is presented. The properties of this method are explored and demonstrated. A uniform sampling method is then proposed for an arbitrary number of sampling points. Experiments are implemented under different sizes of permutation spaces and the results show that the method proposed in this paper has superior performance, which is more than 100 times better than the random method. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT), WSNs are crucial components because they sense, acquire data and communicate with the base station. Because IoT connects devices with scarce resources, the energy needed for communication is viewed as one of the most challenging issues facing WSN assisted IoT. Clustering techniques have the potential to conserve energy and keep network nodes running for longer periods of time. Traditional hierarchical routing protocols are based on a random probability equation for cluster head (CH) selection. Moreover, there is scope to enhance the network lifespan by improving the CH selection approach. To address this, we present the hybrid K-means ant Lion optimization approach for Energy-efficient clustering based Routing (K-LionER) scheme for WSN supported by the IoT. The proposed K-LionER focuses on prolonging the network lifespan and improving energy efficiency. The clusters in WSN under investigation are created using K-means and each CH is chosen using ant lion optimization. CHs acquire the data from cluster members and transmit the agglomerated data to the base station. K-LionER selects the CH based on routing metrics, Remnant Energy (RE), distance between the CHs and Base station (CBD) and Intra-cluster Communication Cost (ICC). A comprehensive simulation is carried out on MATLAB 2017a. K-LionER's accomplishment is contrasted with LEACH, ECFU and GADA-LEACH. The simulation's outcome reveals gains in performance in various aspects, such as alive nodes, stability period, dead nodes and network lifetime metrics. In comparison to the aforementioned routing protocols, the proposed K-LionER protocol improves the network's lifetime by 10% to 48%. [ABSTRACT FROM AUTHOR]

The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative heuristic simulation procedure. The convergence of the gain values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS) Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional Integral and Derivative (PID) controllers. With the kp, ki, kd controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers' variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search, and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers marginally in relation to the others (optimization methods). [ABSTRACT FROM AUTHOR]

This study addresses the problem of scheduling machining operations in a highly automated manufacturing environment while considering the work styles of workers. In actual manufacturing, many aspects of the operation must be considered, such as constraints related to the works to be machined in the machining schedule and the states of the workers. To derive good solutions for such a large-scale problem with many constraints within a realistic amount of computation time, we develop an optimization technique based on a mixed-integer programming (MIP)-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments on a problem based on actual machining requirements are performed to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed CMRWGOA, which combines both Random Weight (shorted RWGOA) and Cauchy mutation (termed CMGOA) mechanism into the GOA. The GOA received inspiration from the foraging and swarming habits of grasshoppers. The performance of the CMRWGOA was validated by 23 benchmark functions in comparison with four well-known meta-heuristic algorithms (AHA, DA, GOA, and MVO), CMGOA, RWGOA, and the GOA. The non-parametric Wilcoxon, Friedman, and Nemenyi statistical tests are conducted on the CMRWGOA. Furthermore, the CMRWGOA has been evaluated in three real-life challenging optimization problems as a complementary study. Various strictly extensive experimental results reveal that the CMRWGOA exhibit better performance. [ABSTRACT FROM AUTHOR]

Mutual funds remain the most favoured investment instrument in the extensive domain of finance due to healthy and blooming returns in its asset group. In order to take investment decision and gain maximum benefit from the highly evolving universal financial market with minimum risk, forecasting Net Asset Value (NAV) of mutual funds is an absolute necessity. This paper presents a NAV predictor model which is designed using the non-iterative Random Vector Functional Link Network (RVFLN). Further, the parameters of the model have been calibrated using a hybrid chaotic meta-heuristic technique that includes Differential Evolution (DE) algorithm and Flower Pollination Algorithm (FPA) in its learning stage. With the application of direct link between input and output nodes and randomized weights in RVFLN, an attempt has been made to enhance the prediction capability of the model by integrating the natural evolution features of DE and the pollination process of FPA along with chaos theory. Validation of efficacy of the proposed optimization technique (CDFPA) is accomplished using ten standard benchmark functions by assessing the convergence pattern against popular and recent meta-heuristics. The proposed RVFLN-CDFPA NAV forecasting model has been applied on real time data sets of three popular Indian mutual funds to predict one day ahead NAV. Also, this research presents a comparison analysis to look into the consequences of various activation functions and popular chaotic maps on the performance of the suggested model. The potential of the novel integrated model has finally been compared with other state-of-the-art approaches. The proposed model exhibits an improved performance over other RVFLN based predictor models like RVFLN-CHFPA, RVFLN-DFPA, RVFLN-FPA, RVFLN-DE, RVFLN-PSO, RVFLN-AO, RVFLN-RSA, RVFLN-AOA and RVFLN-SCA. Also, the novel integrated framework exhibits an improved performance of 61.37%, 72.41%, 71.16% and 69.61% in RMSE over RVFLN, ELM, CPNN-FPA and LPNN-CHFPA, respectively, for DSP fund. For UTI, an improvement of 64.54%, 83.71%, 83.30% and 55.70% in RMSE and for SBI, an improvement of 81.35%, 88.16%, 83.55% and 82.80% in RMSE are reported over RVFLN, ELM, CPNN-FPA and LPNN-CHFPA, respectively, which clearly reveal the competency of the proposed framework over other experimented models. [ABSTRACT FROM AUTHOR]

In modern banks, establishing efficient and quality customer services plays a crucial role to achieve the business success. There are a large number of customers to be served daily; however, required service resources (in particular, customer service representatives) can be rather limited. Therefore, customer service representative scheduling is a challenging problem for bank managers. In the present paper, we focus on a customer service representative scheduling problem considering the stochastic customer arrival time and service duration to minimise the total cost including two parts: (i) the loss concerning the customer waiting and (ii) cost of providing service representatives. We formulate this scheduling problem as an integer programming problem and employ an adapted water wave optimization (WWO) algorithm to solve this problem efficiently. We adapt the propagation and refraction operators of WWO according to the solution space of this problem. The results of the conducted computational experiments demonstrate that the adapted WWO algorithm outperforms the considered state-of-the-art evolutionary algorithms in solving a set of test instances. [ABSTRACT FROM AUTHOR]

Abstract The Mountain Gazelle Optimizer (MGO) algorithm has become one of the most prominent swarm-inspired meta-heuristic algorithms because of its outstanding rapid convergence and excellent accuracy. However, the MGO still faces premature convergence, making it challenging to leave the local optima if early-best solutions neglect the relevant search domain. Therefore, in this study, a newly developed Chaotic-based Mountain Gazelle Optimizer (CMGO) is proposed with numerous chaotic maps to overcome the above-mentioned flaws. Moreover, the ten distinct chaotic maps were simultaneously incorporated into MGO to determine the optimal values and enhance the exploitation of the most promising solutions. The performance of CMGO has been evaluated using CEC2005 and CEC2019 benchmark functions, along with four engineering problems. Statistical tests like the t-test and Wilcoxon rank-sum test provide further evidence that the proposed CMGO outperforms the existing eminent algorithms. Hence, the experimental outcomes demonstrate that the CMGO produces successful and auspicious results.

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

Multispectral image classification is a field of static learning with non-stationary input data assumptions. The evolution of Industry 4.0 has resulted in the development of multispectral images in several application areas. The classification of multispectral images is a tedious process due to its complex characteristics of including spectral as well as spatial features. The recent developments of machine learning (ML) and deep learning (DL) paves a way for the design of effectual computer vision tasks such as object recognition, classification, etc. With this motivation, this paper presents an Invasive Weed Optimization with Capsule Network for Multispectral Image Classification (IWOCN-MSIC) technique. The major intention of the IWOCN-MSIC technique incorporates multilevel discrete wavelet transform (DWT) based image decomposition technique, which decomposes the input image into distinct subbands. Besides, IWO algorithm with Capsule Network (CapsNet) model is applied to derive a useful subset of feature vectors. The complex computation in Capsule Networks(CapsNet) requires hyper-parameters to achieve high classification outputs, which requires more computational time and effort. To overcome this difficulty, a bio-inspired meta-heuristic strategy based Invasive Weed optimization is proposed in this research paper. It allows one to automatically search for and select the appropriate values of CapsNet hyper-parameters. Finally, Deep Denoising Auto-Encoder (DDAE) model is employed for the detection and classification of multispectral images. The experimental result analysis of the IWOCN-MSIC technique reported the better performance of the IWOCN-MSIC technique over the recent state of art approaches. [ABSTRACT FROM AUTHOR]

Human Learning Optimization (HLO) is an emergent inborn binary meta-heuristic inspired by human learning mechanisms. To solve continuous problems efficiently, continuous HLO (CHLO) variants were presented. However, the research on CHLO is at its very initial stage and we find that the underlying cooperation between the learning operators in previous CHLO variants is not ideal, which degrades the overall search ability. To tackle this drawback, this paper develops a new social learning operator (SLO) and presents a novel CHLO with Enhanced Exploration and Exploitation (CHLOEEE), in which the new SLO can efficiently explore a wider area and consequently strengthens the exploration ability. Thanks to the new SLO, the individual learning operator in CHLOEEE is totally relieved from the exploration duty and focuses on local search to further enhance the exploitation capability. By comparing and analyzing the search behaviors of CHLO variants, the advantages of CHLOEEE are clearly explained and the insight of the inherent mechanisms is revealed. Finally, the proposed CHLOEEE is evaluated on the CEC15, CEC17, and engineering benchmark problems and compared with the previous CHLO variants and other nine state-of-the-art meta-heuristic algorithms. The results show that CHLOEEE is superior to the latest CHLO variant, named CHLOEE, on two-thirds of all the benchmark functions and significantly outperforms the other algorithms on at least 12 out of 15 CEC15 functions and 21 out of 30 CEC17 functions. Furthermore, CHLOEEE achieves the best results on all the 9 engineering optimization problems. [ABSTRACT FROM AUTHOR]

As environmental contamination becomes more and more severe, enterprises need to consider optimizing environmental criteria while optimizing production criteria. In this study, a multi-objective green flexible job shop scheduling problem (MO_GFJSP) is established with two objective functions: the makespan and the carbon emission. To effectively solve the MO_GFJSP, an improved chimp optimization algorithm (IChOA) is designed. The proposed IChOA has four main innovative aspects: 1) the fast non-dominated sorting (FDS) method is introduced to compare the individuals with multiple objectives and strengthen the solution accuracy. 2) a dynamic convergence factor (DCF) is introduced to strengthen the capabilities of exploration and exploitation. 3) the position weight (PW) is used in the individual position updating to enhance the search efficiency. 4) the variable neighborhood search (VNS) is developed to strengthen the capacity to escape the local optimum. By executing abundant experiments using 20 benchmark instances, it was demonstrated that the developed IChOA is efficient to solve the MO_GFJSP and effective for reducing carbon emission in the flexible job shop. [ABSTRACT FROM AUTHOR]

Assembly lines are widely used mass production techniques applied in various industries from electronics to automotive and aerospace. A branch, bound, and remember (BBR) algorithm is presented in this research to tackle the chance-constrained stochastic assembly line balancing problem (ALBP). In this problem variation, the processing times are stochastic, while the cycle time must be respected for a given probability. The proposed BBR method stores all the searched partial solutions in memory and utilizes the cyclic best-first search strategy to quickly achieve high-quality complete solutions. Meanwhile, this study also develops several new lower bounds and dominance rules by taking the stochastic task times into account. To evaluate the performance of the developed method, a large set of 1614 instances is generated and solved. The performance of the BBR algorithm is compared with two mixed-integer programming models and twenty re-implemented heuristics and metaheuristics, including the well-known genetic algorithm, ant colony optimization algorithm and simulated annealing algorithm. The comparative study demonstrates that the mathematical models cannot achieve high-quality solutions when solving large-size instances, for which the BBR algorithm shows clear superiority over the mathematical models. The developed BBR outperforms all the compared heuristic and metaheuristic methods and is the new state-of-the-art methodology for the stochastic ALBP. [ABSTRACT FROM AUTHOR]

Due to a large number of design variables involved in the optimization of RC structures, a multi stage cascade optimization is used. This algorithm speeds up an accurate optimal design for the large-scale structures reducing the number of variables by dividing single optimization to a number of stages such that the optimization of each stage starts with the optimum results of the previous one. Here, the first stage of cascade optimization method uses the assembling of the stiffness matrix of the entire RC structure for minimizing the torsion of the stories as an objective function. By assembling the stiffness matrix of the RC frame without shear walls and the shear wall alone, the length and thickness of the shear wall on each story are taken as design variables of the first stage. The optimized reinforcement arrangement of walls according to the required wall rebar area is the goal of the next stage. Using this method, designing the RC structure and minimizing the structural torsion of each floor simultaneously, can result in different length and thickness for the shear walls in different stories. Reducing the structural torsion leads to economical structure. Here, the MATLAB and ETABS interfacing are utilized. [ABSTRACT FROM AUTHOR]

Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods. [ABSTRACT FROM AUTHOR]

Satellite images usually have relatively narrow brightness value ranges which entails a demand to broaden the contrast and retain relevant details contained in the images prior to further analysis of the images. Improving the contrast and visual information quality of images is a pre-step in image analysis as image quality poses a key role in improving human perception and understanding of images. Meta-heuristics have been efficiently utilized to solve complicated image processing problems. This work presents an improved version of a meta-heuristic called Chameleon Swarm Algorithm (CSA) hybridized with Crow Search Optimizer (CSO), referred to as CCSA, to ameliorate contrast and brightness in satellite images. In the developed method, the following improvements were applied: 1) amendments to the speed of CSA's individuals, 2) inclusion of memory for CSA's individuals, and 3) hybridization of CSA with CSO. The CCSA method is employed to identify the optimal pixels' values that can condense hidden maximization of low-contrast satellite images. This is carried out by maximizing a fitness method with a view to improve the local contrast, local brightness, and details of edges of the images. The proposed CCSA is examined on openly handy satellite images along with some norm images. Experimental results showed the robustness and efficiency of CCSA in reliably enhancing brightness and contrast as well as retaining more image details of satellite images. The performance of CCSA was then put in comparison with other popular image enhancement algorithms and other nine methods developed based on existing meta-heuristics. The results of both qualitative and quantitative evaluation methods proved that CCSA outperforms other algorithms, considered here for comparison, to a greater extent. In more detail, the results on 10 color images divulged that the performance of CCSA regarding the average feature similarity index, average structural contrast - quality index, and average Michelson contrast were more than 96.93%, 99.91%, and 96.11%, respectively. These findings are close to unity and are superior to all other rivals in the corresponding assessment methods. [ABSTRACT FROM AUTHOR]

Economic and safe structural design of high-rise buildings is a high-dimensional optimization problem. Finding the globally optimal solution is challenging, and analysis, design and optimization are very time consuming. This article proposes a new approach, where the search space dimension is reduced through substructuring. The high-dimensional high-rise building optimization problem is converted to a set of low-rise building optimization subproblems of lower dimensions. The subproblems are iteratively solved instead of the original problem. The substructures are analysed and optimally designed, one after another, until convergence, taking into account the effects of other parts of the building to maintain the building's integrity. Thus, the analysis of a high-rise building is accomplished by analysing a set of low-rise building substructures. Concurrently, the high-dimensional optimization problem is solved through some low-dimensional subproblems. Therefore, building analysis, design and optimization are conducted simultaneously. To demonstrate the efficiency and robustness of the approach, examples from the literature aresolved. [ABSTRACT FROM AUTHOR]