PSO with Mixed Strategy for Global Optimization
Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/7111548 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546578050580480 |
|---|---|
| author | Jinwei Pang Xiaohui Li Shuang Han |
| author_facet | Jinwei Pang Xiaohui Li Shuang Han |
| author_sort | Jinwei Pang |
| collection | DOAJ |
| description | Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms. |
| format | Article |
| id | doaj-art-0e03f91a42cb459795bc93d8fa7c09f4 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0e03f91a42cb459795bc93d8fa7c09f42025-02-03T06:47:40ZengWileyComplexity1099-05262023-01-01202310.1155/2023/7111548PSO with Mixed Strategy for Global OptimizationJinwei Pang0Xiaohui Li1Shuang Han2School of Computer and Control EngineeringHarbin Vocational and Technical CollegeDepartment of Computer Science and TechnologyParticle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.http://dx.doi.org/10.1155/2023/7111548 |
| spellingShingle | Jinwei Pang Xiaohui Li Shuang Han PSO with Mixed Strategy for Global Optimization Complexity |
| title | PSO with Mixed Strategy for Global Optimization |
| title_full | PSO with Mixed Strategy for Global Optimization |
| title_fullStr | PSO with Mixed Strategy for Global Optimization |
| title_full_unstemmed | PSO with Mixed Strategy for Global Optimization |
| title_short | PSO with Mixed Strategy for Global Optimization |
| title_sort | pso with mixed strategy for global optimization |
| url | http://dx.doi.org/10.1155/2023/7111548 |
| work_keys_str_mv | AT jinweipang psowithmixedstrategyforglobaloptimization AT xiaohuili psowithmixedstrategyforglobaloptimization AT shuanghan psowithmixedstrategyforglobaloptimization |