A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies

Particle Swarm Optimization (PSO), a meta-heuristic algorithm inspired by swarm intelligence, is widely applied to various optimization problems due to its simplicity, ease of implementation, and fast convergence. However, PSO frequently converges prematurely to local optima when addressing single-o...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 4518 - 49
Hlavní autoři: Xu, Huarong, Deng, Qianwei, Zhang, Zhiyu, Lin, Shengke
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 06.02.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/705/1042
639/705/117
Algorithms
Center nearest particle
Competitiveness
Convergence
Differential evolution (DE)
Dynamic strategies
Evolution
Humanities and Social Sciences
multidisciplinary
Mutation
Mutation crossover operator
Optimization algorithms
Particle swarm optimization (PSO)
Science
Science (multidisciplinary)
Mutation crossover operator
Particle swarm optimization (PSO)
Dynamic strategies
Differential evolution (DE)
Center nearest particle
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:Particle Swarm Optimization (PSO), a meta-heuristic algorithm inspired by swarm intelligence, is widely applied to various optimization problems due to its simplicity, ease of implementation, and fast convergence. However, PSO frequently converges prematurely to local optima when addressing single-objective numerical optimization problems due to its inherent rapid convergence. To address this issue, we propose a hybrid differential evolution (DE) particle swarm optimization algorithm based on dynamic strategies (MDE-DPSO). In our proposed algorithm, we first introduce a novel dynamic inertia weight method along with adaptive acceleration coefficients to dynamically adjust the particles’ search range. Secondly, we propose a dynamic velocity update strategy that integrates the center nearest particle and a perturbation term. Finally, the mutation crossover operator of DE is applied to PSO, selecting the appropriate mutation strategy based on particle improvement, which generates a mutant vector. This vector is then combined with the current particle’s best position through crossover, aiding particles in escaping local optima. To validate the efficacy of MDE-DPSO, we evaluated it on the CEC2013, CEC2014, CEC2017, and CEC2022 benchmark suites, comparing its performance against fifteen algorithms. The experimental results indicate that our proposed algorithm demonstrates significant competitiveness.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-82648-5