A hybrid particle swarm optimization algorithm for solving engineering problem
Abstract To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is u...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-59034-2 |
| _version_ | 1797209376233095168 |
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| author | Jinwei Qiao Guangyuan Wang Zhi Yang Xiaochuan Luo Jun Chen Kan Li Pengbo Liu |
| author_facet | Jinwei Qiao Guangyuan Wang Zhi Yang Xiaochuan Luo Jun Chen Kan Li Pengbo Liu |
| author_sort | Jinwei Qiao |
| collection | DOAJ |
| description | Abstract To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( $${f}_{1}-{f}_{13}$$ f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. |
| first_indexed | 2024-04-24T09:53:43Z |
| format | Article |
| id | doaj.art-d79bb352a58b43ff965c43389f22e304 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T09:53:43Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d79bb352a58b43ff965c43389f22e3042024-04-14T11:15:16ZengNature PortfolioScientific Reports2045-23222024-04-0114113010.1038/s41598-024-59034-2A hybrid particle swarm optimization algorithm for solving engineering problemJinwei Qiao0Guangyuan Wang1Zhi Yang2Xiaochuan Luo3Jun Chen4Kan Li5Pengbo Liu6School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences)School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences)School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences)School of Information Science and Engineering, Northeastern UniversitySchool of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences)Fushun Supervision Inspection Institute for Special EquipmentSchool of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences)Abstract To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( $${f}_{1}-{f}_{13}$$ f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems.https://doi.org/10.1038/s41598-024-59034-2Particle swarm optimizationElite opposition-based learningIterative mappingConvergence analysis |
| spellingShingle | Jinwei Qiao Guangyuan Wang Zhi Yang Xiaochuan Luo Jun Chen Kan Li Pengbo Liu A hybrid particle swarm optimization algorithm for solving engineering problem Scientific Reports Particle swarm optimization Elite opposition-based learning Iterative mapping Convergence analysis |
| title | A hybrid particle swarm optimization algorithm for solving engineering problem |
| title_full | A hybrid particle swarm optimization algorithm for solving engineering problem |
| title_fullStr | A hybrid particle swarm optimization algorithm for solving engineering problem |
| title_full_unstemmed | A hybrid particle swarm optimization algorithm for solving engineering problem |
| title_short | A hybrid particle swarm optimization algorithm for solving engineering problem |
| title_sort | hybrid particle swarm optimization algorithm for solving engineering problem |
| topic | Particle swarm optimization Elite opposition-based learning Iterative mapping Convergence analysis |
| url | https://doi.org/10.1038/s41598-024-59034-2 |
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