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This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. [Display omitted] • A detailed overview of optimization and its variants. • A literature review on state-of-the-art algorithms from 1965 to 2022. • Statistical analyzes and explanations in each year and during the 57 years. [ABSTRACT FROM AUTHOR]

This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain's sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations' sustainability, efficiency, and competitiveness. [ABSTRACT FROM AUTHOR]

Multi-robot path planning (MRPP) in continuous and known environment is studied in this paper via proposing a novel local path planning approach. To plan optimal collision-free paths for multiple robots simultaneously, a novel implementation method suitable for the meta-heuristic algorithms is devised, and an improved artificial bee colony (ABC) algorithm is developed. Three enhancements to the ABC algorithm are made in this context. Firstly, to better lead the search direction, the global best individual is involved in the search equations of employed bee phase and scout bee phase. Meanwhile, to boost exploitation capability, the learning method of teaching-learning based optimization (TLBO) algorithm is incorporated into the onlooker bee phase. The proposed learning-based ABC (ABCL) algorithm is used to determine the subsequent positions for all the robots based on their current coordinates considering the path length, safety and planning efficiency. The experimental studies on benchmark functions show that ABCL is outstanding in solving different types of optimization problems compared against seven effective meta-heuristic algorithms. More importantly, MRPP simulation results prove that ABCL outperforms its competitors in terms of generating optimal collision-free paths and running time. Compared with the original ABC, ABCL improves these two aspects on average for all tasks by 2.1% and 12.6%, respectively. Therefore, with the proposed implementation method, ABCL can be considered as an effective MRPP solution. • A metaheuristic-based approach is proposed for the multi-robot path planning problem. • A learning-based ABC algorithm is developed to solve time-sensitive online problems. • Simulations of different MRPP tasks were completed, validating the designed approach. [ABSTRACT FROM AUTHOR]

The Newton-Raphson-Based Optimizer (NRBO), a new metaheuristic algorithm, is suggested and developed in this paper. The NRBO is inspired by Newton-Raphson's approach, and it explores the entire search process using two rules: the Newton-Raphson Search Rule (NRSR) and the Trap Avoidance Operator (TAO) and a few groups of matrices to explore the best results further. The NRSR uses a Newton-Raphson method to improve the exploration ability of NRBO and increase the convergence rate to reach improved search space positions. The TAO helps the NRBO to avoid the local optima trap. The performance of NRBO was assessed using 64 benchmark numerical functions, including 23 standard benchmarks, 29 CEC2017 constrained benchmarks, and 12 CEC2022 benchmarks. In addition, the NRBO was employed to optimize 12 CEC2020 real-world constrained engineering optimization problems. The proposed NRBO was compared to seven state-of-the-art optimization algorithms, and the findings showed that the NRBO produced promising results due to its features, such as high exploration and exploitation balance, high convergence rate, and effective avoidance of local optima capabilities. In addition, the NRBO also validated on challenging wireless communication problem called the internet of vehicle problem, and the NRBO was able to find the optimal path for data transmission. Also, the performance of NRBO in training the deep reinforcement learning agents is also studied by considering the mountain car problem. The obtained results also proved the NRBO's excellent performance in handling challenging real-world engineering problems. [ABSTRACT FROM AUTHOR]

Operations Research (OR) analytics play a crucial role in optimizing decision-making processes for real-world problems. The assignment problem, with applications in supply chains, healthcare logistics, and production scheduling, represents a prominent optimization challenge. This paper focuses on addressing the Generalized Quadratic Assignment Problem (GQAP), a well-known NP-hard combinatorial optimization problem. To tackle the GQAP, we propose an OR analytical approach that incorporates efficient relaxations, reformulations, heuristics, and a metaheuristic algorithm. Initially, we employ the Reformulation Linearization Technique (RLT) to generate various linear relaxation models, carefully selecting the most efficient ones. Building upon this foundation, we introduce a robust reformulation based on Benders Decomposition (BD), which serves as the basis for an iterative optimization algorithm applied to the GQAP. Furthermore, we develop a constructive heuristic algorithm to identify near-optimal solutions, followed by an enhancement utilizing an Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm. This ALNS algorithm is enhanced through the integration of a tabu list derived from Tabu Search (TS) and a decision rule inspired by Simulated Annealing (SA). To validate our approach and evaluate its performance, we conduct a comparative analysis against state-of-the-art algorithms documented in the literature. This comparison confirms the significant improvements achieved in terms of solution quality and computational efficiency through our proposed methodology. These advancements contribute to the state of the art in solving the GQAP and hold the potential to enhance decision-making processes in a wide range of domains. Our methodology demonstrates remarkable improvements in solution quality and computational efficiency when compared to existing approaches, as evidenced by the comparative results with state-of-the-art algorithms. The potential implications of our research extend to optimizing decision-making processes in diverse fields, rendering it highly relevant and impactful. • Analyzing different reformulation linearization inequalities for the GQA. • Introducing a novel Benders decomposition reformulation method for the GQAP. • Developing a new constructive heuristic algorithm for the initial solution of the GQAP. • Proposing different removal–insertion​ heuristics and a local search using a tabu list. • Introducing an efficient adaptive large neighborhood search algorithm for solving the GQAP. [ABSTRACT FROM AUTHOR]

Distributed permutation flowshop scheduling problem (DPFSP) has always been a hot issue. The optimization goal of minimizing the total flowtime is of great significance to the environment of multi-factory. In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed to solve the DPFSP with the total flowtime criterion. In the proposed DFFO, an initialization method considering the population quality and diversity is adopted. In the smell-based search stage, three perturbation operators, the Shift-based operator, the Exchange-based operator and the Hybrid operator are designed respectively, and each fruit fly improves its state through a specific neighborhood strategy. In addition, we propose an improved reference local search (MRLS) method to enhance the exploitation ability of fruit flies. In the vision-based search stage, fruit flies use a well-designed combination update mechanism to lead fruit flies to more potential areas. In order to enhance the exploration ability, we use random reinforcement method for the population. The parameters are evaluated using an orthogonal experimental design to determine the appropriate values of the key parameters. In addition, we test the DFFO and the state-of-art algorithms from the literature on 720 large-scale instances. The experimental results show that DFFO is a very effective metaheuristic. [ABSTRACT FROM AUTHOR]

As one of the most promising renewable energies, solar energy can be converted to electricity through photovoltaic (PV) systems. It is indispensable to identify the parameters of PV systems with the aim of controlling and simulating. Thanks to the complexity of PV systems, parameter identification is still a challenging task. In this paper, we develop a Ranking Teaching–Learning-Based​ Optimization (RTLBO) to solve the problem, in which Teaching–Learning-Based​ Optimization (TLBO) is a population-based swarm algorithm and mimics the learning process in a classroom. RTLBO ranks learners into superior and inferior groups, in which the outstanding learners learn from the top three agents to boost the local search. In contrast, the low learners learn from each other by guidance. The two phases are in parallel to balance the local and global search. The proposed RTLBO is used to extract parameters of different models, including the single diode model, double diode model and three PV module models. TLBO, four TLBO variants, and fifteen meta-heuristic algorithms are selected as the rivals of RTLBO. Several experiments have shown that our method is a reliable and effective algorithm when addressing the parameters of PV systems. [ABSTRACT FROM AUTHOR]

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm Optimization and Differential Evolution. Their initial versions were very simple, but rapidly attracted wide attention. During the last quarter century hundreds of variants of both optimization algorithms have been proposed and applied in almost any field of science or engineering. However, no broader comparison of performance between both families of methods has been presented so far. In the present paper ten Particle Swarm Optimization and ten Differential Evolution variants, from historical ones from the 1990s up to the most recent ones from 2022, are compared on numerous single-objective numerical benchmarks and 22 real-world problems. On average Differential Evolution algorithms clearly outperform Particle Swarm Optimization ones. Such advantage of Differential Evolution over Particle Swarm Optimization is in contradiction with popularity: In the literature Particle Swarm Optimization algorithms are two–three times more frequently used than Differential Evolution ones. Problems for which Particle Swarm Optimization performs better than Differential Evolution do exist but are relatively few. Although this result may be an effect of the choice of specific variants, experimental settings or problems used for comparison, some re-consideration of algorithmic philosophy may be needed for Particle Swarm Optimization variants to make them more competitive. [ABSTRACT FROM AUTHOR]