Learning Competitive Swarm Optimization

Borowska, Bożena

doi:10.3390/e24020283

Cited by 5 publications

(4 citation statements)

References 50 publications

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“…The GA was integrated with the local search strategy. Moreover, many excellent improved algorithms (RPSO (Borowska [ 72 ]), JADE (Su et al [ 73 ]), L-SHADE (Chen et al [ 74 ]), learning CSO (Borowska [ 75 ]), and CMA-ES (Tong et al [ 76 ])) should also be used to comprehensively analyze the performance of the proposed algorithm, and this study selected two of them (RPSO, JADE). In order to verify the effectiveness and superiority of the MG-ChOA algorithm, this paper also comprehensively compares the convergence curve, ANOVA test, fitness value obtained by 30 runs, Wilcoxon rank-sum test (Gibbons et al [ 77 ]; Derrac et al [ 78 ]), effects of different improvement methods, and running results.…”

Section: Resultsmentioning

confidence: 99%

An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time Windows

et al. 2022

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The vehicle routing problem with time windows (VRPTW) is a classical optimization problem. There have been many related studies in recent years. At present, many studies have generally analyzed this problem on the two-dimensional plane, and few studies have explored it on spherical surfaces. In order to carry out research related to the distribution of goods by unmanned vehicles and unmanned aerial vehicles, this study carries out research based on the situation of a three-dimensional sphere and proposes a three-dimensional spherical VRPTW model. All of the customer nodes in this problem were mapped to the three-dimensional sphere. The chimp optimization algorithm is an excellent intelligent optimization algorithm proposed recently, which has been successfully applied to solve various practical problems and has achieved good results. The chimp optimization algorithm (ChOA) is characterized by its excellent ability to balance exploration and exploitation in the optimization process so that the algorithm can search the solution space adaptively, which is closely related to its outstanding adaptive factors. However, the performance of the chimp optimization algorithm in solving discrete optimization problems still needs to be improved. Firstly, the convergence speed of the algorithm is fast at first, but it becomes slower and slower as the number of iterations increases. Therefore, this paper introduces the multiple-population strategy, genetic operators, and local search methods into the algorithm to improve its overall exploration ability and convergence speed so that the algorithm can quickly find solutions with higher accuracy. Secondly, the algorithm is not suitable for discrete problems. In conclusion, this paper proposes an improved chimp optimization algorithm (MG-ChOA) and applies it to solve the spherical VRPTW model. Finally, this paper analyzes the performance of this algorithm in a multi-dimensional way by comparing it with many excellent algorithms available at present. The experimental result shows that the proposed algorithm is effective and superior in solving the discrete problem of spherical VRPTW, and its performance is superior to that of other algorithms.

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Section: Resultsmentioning

confidence: 99%

An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time Windows

et al. 2022

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“…Hybrid PSO algorithms are combined with some traditional and evolutionary optimization methods in order to utilized the advantages of both methods and improve the global search ability of PSO, such as simulated annealing (SA) [51], tabu search (TS) [52], BBO [53], artificial bee colony (ABC) [54], genetic algorithm (GA) [55] and differential evolution (DE) [56]. Speciality, multi-swarm PSO is an important field of improved PSO in recent years [57][58][59][60]. PSO has been widely used to solve practical engineering problems due to easy implementation and robust performance [61], including clustering problems [62], signalized traffic problems [63], image segmentation [64], feature selection [65], antenna synthesis [66] and fuzzy controlled systems [67].…”

Section: Related Workmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

Mathematics

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An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

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“…PSO is another branch of EC that operates based on swarm-based intelligence. Its implementation is simple and easy, but it also suffers from premature convergence [18,28]. Therefore, various modifications are applied to the standard PSO to overcome its drawback.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

et al. 2022

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Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality.

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Learning Competitive Swarm Optimization

Cited by 5 publications

References 50 publications

An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time Windows

An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time Windows

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

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