| Wu Jin (吴 进),Xiong Hao,Luo Wenxuan,Hao Chengbin.[J].高技术通讯(英文),2026,32(1):30~38 |
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| Multi-strategy improved sand cat swarm optimization based on somersault pursuit and adaptive Lévy flight |
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| DOI：10. 3772 / j. issn. 1006-6748. 2026. 01. 004 |
| 中文关键词: |
| 英文关键词: sand cat swarm optimization, Kent chaotic mapping, somersault pursuit, adaptive Lévy flight, vertical and horizontal crossover |
| 基金项目: |
| Author Name | Affiliation | | Wu Jin (吴 进) | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China) | | Xiong Hao | | | Luo Wenxuan | | | Hao Chengbin | |
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| 中文摘要: |
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| 英文摘要: |
| To address the limitations of the sand cat swarm optimization (SCSO) algorithm which are slow convergence and low accuracy in complex problems, this study proposes an improved SCSO (ISCSO) algorithm that integrates multiple enhancement strategies. Firstly, Kent chaotic mapping initializes the population for uniform distribution. Secondly, somersault foraging strategy is introduced during the search and attack phases, allowing the algorithm to escape local optima by intercepting evasive prey. Simultaneously, an adaptive Lévy flight strategy is incorporated into the attack phase to bolster global exploration. Finally, the vertical and horizontal crossover strategy is implemented to enhance population diversity. The performance of the proposed algorithm is evaluated using 16 benchmark test functions. The experimental results demonstrate that ISCSO significantly outperforms the original SCSO and shows notable advantages over other metaheuristic algorithms. Furthermore,application to a pressure vessel design problem verifies ISCSO’s effectiveness in solving practical engineering optimization challenges. |
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