Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy
Abstract Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distanc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-07-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-022-00099-0 |
| _version_ | 1818190097092182016 |
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| author | Zhentao Tang Sichen Tao Kaiyu Wang Bo Lu Yuki Todo Shangce Gao |
| author_facet | Zhentao Tang Sichen Tao Kaiyu Wang Bo Lu Yuki Todo Shangce Gao |
| author_sort | Zhentao Tang |
| collection | DOAJ |
| description | Abstract Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original selection method, and add chaotic local search with selecting chaotic map based on memory to further improve the search performance of the algorithm. A chaotic wind driven optimization with fitness-distance balance strategy is proposed, called CFDBWDO. In the experimental section, we find the optimal parameter settings for the proposed algorithm. To verify the effect of the algorithm, we conduct comparative experiments on the CEC 2017 benchmark functions. The experimental results denote that the proposed algorithm has superior performance. Compared with WDO, CFDBWDO can gradually converge in function optimization. We further verify the practicality of the proposed algorithm with six real-world optimization problems, and the obtained results are all better than other algorithms. |
| first_indexed | 2024-12-11T23:53:17Z |
| format | Article |
| id | doaj.art-4ec64c846a384ed69b83ab844e39d78e |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T23:53:17Z |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-4ec64c846a384ed69b83ab844e39d78e2022-12-22T00:45:24ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832022-07-0115112810.1007/s44196-022-00099-0Chaotic Wind Driven Optimization with Fitness Distance Balance StrategyZhentao Tang0Sichen Tao1Kaiyu Wang2Bo Lu3Yuki Todo4Shangce Gao5Faculty of Engineering, University of ToyamaFaculty of Engineering, University of ToyamaFaculty of Engineering, University of ToyamaFaculty of Engineering, Shanghai Normal University Tianhua CollegeFaculty of Electrical, Information and Communication Engineering, Kanazawa UniversityFaculty of Engineering, University of ToyamaAbstract Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original selection method, and add chaotic local search with selecting chaotic map based on memory to further improve the search performance of the algorithm. A chaotic wind driven optimization with fitness-distance balance strategy is proposed, called CFDBWDO. In the experimental section, we find the optimal parameter settings for the proposed algorithm. To verify the effect of the algorithm, we conduct comparative experiments on the CEC 2017 benchmark functions. The experimental results denote that the proposed algorithm has superior performance. Compared with WDO, CFDBWDO can gradually converge in function optimization. We further verify the practicality of the proposed algorithm with six real-world optimization problems, and the obtained results are all better than other algorithms.https://doi.org/10.1007/s44196-022-00099-0Wind driven optimizationLocal optimaFitness-distance balance selection methodPopulation diversityChaotic mapChaotic local search |
| spellingShingle | Zhentao Tang Sichen Tao Kaiyu Wang Bo Lu Yuki Todo Shangce Gao Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy International Journal of Computational Intelligence Systems Wind driven optimization Local optima Fitness-distance balance selection method Population diversity Chaotic map Chaotic local search |
| title | Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy |
| title_full | Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy |
| title_fullStr | Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy |
| title_full_unstemmed | Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy |
| title_short | Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy |
| title_sort | chaotic wind driven optimization with fitness distance balance strategy |
| topic | Wind driven optimization Local optima Fitness-distance balance selection method Population diversity Chaotic map Chaotic local search |
| url | https://doi.org/10.1007/s44196-022-00099-0 |
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