A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies
Abstract 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...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82648-5 |
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| author | Huarong Xu Qianwei Deng Zhiyu Zhang Shengke Lin |
| author_facet | Huarong Xu Qianwei Deng Zhiyu Zhang Shengke Lin |
| author_sort | Huarong Xu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-42fd12b3d12841fe982c2fcf8d1ad429 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-42fd12b3d12841fe982c2fcf8d1ad4292025-02-09T12:30:13ZengNature PortfolioScientific Reports2045-23222025-02-0115114910.1038/s41598-024-82648-5A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategiesHuarong Xu0Qianwei Deng1Zhiyu Zhang2Shengke Lin3College of Computer and Information Engineering, Xiamen University of TechnologyCollege of Computer and Information Engineering, Xiamen University of TechnologyCollege of Computer and Information Engineering, Xiamen University of TechnologyCollege of Computer and Information Engineering, Xiamen University of TechnologyAbstract 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.https://doi.org/10.1038/s41598-024-82648-5Particle swarm optimization (PSO)Differential evolution (DE)Dynamic strategiesCenter nearest particleMutation crossover operator |
| spellingShingle | Huarong Xu Qianwei Deng Zhiyu Zhang Shengke Lin A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies Scientific Reports Particle swarm optimization (PSO) Differential evolution (DE) Dynamic strategies Center nearest particle Mutation crossover operator |
| title | A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies |
| title_full | A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies |
| title_fullStr | A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies |
| title_full_unstemmed | A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies |
| title_short | A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies |
| title_sort | hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies |
| topic | Particle swarm optimization (PSO) Differential evolution (DE) Dynamic strategies Center nearest particle Mutation crossover operator |
| url | https://doi.org/10.1038/s41598-024-82648-5 |
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