Multiple strategies improved spider wasp optimization for engineering optimization problem solving
Abstract The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps....
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78589-8 |
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| author | Jinxue Sui Zifan Tian Zuoxun Wang |
| author_facet | Jinxue Sui Zifan Tian Zuoxun Wang |
| author_sort | Jinxue Sui |
| collection | DOAJ |
| description | Abstract The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems. |
| format | Article |
| id | doaj-art-b7139b4de076494b97a51ebe5b4713f4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-b7139b4de076494b97a51ebe5b4713f42024-11-24T12:26:18ZengNature PortfolioScientific Reports2045-23222024-11-0114112910.1038/s41598-024-78589-8Multiple strategies improved spider wasp optimization for engineering optimization problem solvingJinxue Sui0Zifan Tian1Zuoxun Wang2Information and Electronic Engineering, Shandong Technology and Business UniversityInformation and Electronic Engineering, Shandong Technology and Business UniversityInformation and Electronic Engineering, Shandong Technology and Business UniversityAbstract The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems.https://doi.org/10.1038/s41598-024-78589-8Spider wasp optimizerEngineering designAdaptive step size operatorDynamic selectionDynamic lens imaging reverse learning |
| spellingShingle | Jinxue Sui Zifan Tian Zuoxun Wang Multiple strategies improved spider wasp optimization for engineering optimization problem solving Scientific Reports Spider wasp optimizer Engineering design Adaptive step size operator Dynamic selection Dynamic lens imaging reverse learning |
| title | Multiple strategies improved spider wasp optimization for engineering optimization problem solving |
| title_full | Multiple strategies improved spider wasp optimization for engineering optimization problem solving |
| title_fullStr | Multiple strategies improved spider wasp optimization for engineering optimization problem solving |
| title_full_unstemmed | Multiple strategies improved spider wasp optimization for engineering optimization problem solving |
| title_short | Multiple strategies improved spider wasp optimization for engineering optimization problem solving |
| title_sort | multiple strategies improved spider wasp optimization for engineering optimization problem solving |
| topic | Spider wasp optimizer Engineering design Adaptive step size operator Dynamic selection Dynamic lens imaging reverse learning |
| url | https://doi.org/10.1038/s41598-024-78589-8 |
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