Heterogeneous differential evolution particle swarm optimization with local search
Abstract To develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteri...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-05-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01082-8 |
| _version_ | 1797647113649127424 |
|---|---|
| author | Anping Lin Dong Liu Zhongqi Li Hany M. Hasanien Yaoting Shi |
| author_facet | Anping Lin Dong Liu Zhongqi Li Hany M. Hasanien Yaoting Shi |
| author_sort | Anping Lin |
| collection | DOAJ |
| description | Abstract To develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteristics of learning exemplars for PSO, one DE mutant is for enhancing exploration and the other is for enhance exploitation. To further improve search accuracy in the late stage of optimization, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) local search is employed. To assess the performance of HeDE-PSO, it is tested on the CEC2017 test suite and the industrial refrigeration system design problem. The test results are compared with seven recent PSO algorithms, JADE (adaptive differential evolution with optional external archive) and four meta-heuristics. The comparison results show that with two DE mutants to construct learning exemplars, HeDE-PSO can balance exploration and exploitation and obtains strong adaptability on different kinds of optimization problems. On 10-dimensional functions and 30-dimensional functions, HeDE-PSO is only outperformed by the most competitive PSO algorithm on seven and six functions, respectively. HeDE-PSO obtains the best performance on sixteen 10-dimensional functions and seventeen-30 dimensional functions. Moreover, HeDE-PSO outperforms other compared PSO algorithms on the industrial refrigeration system design problem. |
| first_indexed | 2024-03-11T15:12:39Z |
| format | Article |
| id | doaj.art-4dbbad38dc5a40ceaaca10307ccc963f |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-11T15:12:39Z |
| publishDate | 2023-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-4dbbad38dc5a40ceaaca10307ccc963f2023-10-29T12:41:04ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-05-01966905692510.1007/s40747-023-01082-8Heterogeneous differential evolution particle swarm optimization with local searchAnping Lin0Dong Liu1Zhongqi Li2Hany M. Hasanien3Yaoting Shi4School of Physics and Electronic Electrical Engineering, Xiangnan UniversitySchool of Computer and Artificial Intelligence, Xiangnan UniversityCollege of Transportation Engineering, Hunan University of TechnologyElectrical Power and Machines Department, Faculty of Engineering, Ain Shams UniversitySchool of Physics and Electronic Electrical Engineering, Xiangnan UniversityAbstract To develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteristics of learning exemplars for PSO, one DE mutant is for enhancing exploration and the other is for enhance exploitation. To further improve search accuracy in the late stage of optimization, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) local search is employed. To assess the performance of HeDE-PSO, it is tested on the CEC2017 test suite and the industrial refrigeration system design problem. The test results are compared with seven recent PSO algorithms, JADE (adaptive differential evolution with optional external archive) and four meta-heuristics. The comparison results show that with two DE mutants to construct learning exemplars, HeDE-PSO can balance exploration and exploitation and obtains strong adaptability on different kinds of optimization problems. On 10-dimensional functions and 30-dimensional functions, HeDE-PSO is only outperformed by the most competitive PSO algorithm on seven and six functions, respectively. HeDE-PSO obtains the best performance on sixteen 10-dimensional functions and seventeen-30 dimensional functions. Moreover, HeDE-PSO outperforms other compared PSO algorithms on the industrial refrigeration system design problem.https://doi.org/10.1007/s40747-023-01082-8Differential evolutionIndustrial refrigeration system designLocal searchParticle swarm optimization |
| spellingShingle | Anping Lin Dong Liu Zhongqi Li Hany M. Hasanien Yaoting Shi Heterogeneous differential evolution particle swarm optimization with local search Complex & Intelligent Systems Differential evolution Industrial refrigeration system design Local search Particle swarm optimization |
| title | Heterogeneous differential evolution particle swarm optimization with local search |
| title_full | Heterogeneous differential evolution particle swarm optimization with local search |
| title_fullStr | Heterogeneous differential evolution particle swarm optimization with local search |
| title_full_unstemmed | Heterogeneous differential evolution particle swarm optimization with local search |
| title_short | Heterogeneous differential evolution particle swarm optimization with local search |
| title_sort | heterogeneous differential evolution particle swarm optimization with local search |
| topic | Differential evolution Industrial refrigeration system design Local search Particle swarm optimization |
| url | https://doi.org/10.1007/s40747-023-01082-8 |
| work_keys_str_mv | AT anpinglin heterogeneousdifferentialevolutionparticleswarmoptimizationwithlocalsearch AT dongliu heterogeneousdifferentialevolutionparticleswarmoptimizationwithlocalsearch AT zhongqili heterogeneousdifferentialevolutionparticleswarmoptimizationwithlocalsearch AT hanymhasanien heterogeneousdifferentialevolutionparticleswarmoptimizationwithlocalsearch AT yaotingshi heterogeneousdifferentialevolutionparticleswarmoptimizationwithlocalsearch |