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Multi-food source differential salp swarm algorithm

Published: 15 December 2023 Publication History

Abstract

Salp swarm algorithm (SSA) divides the population into leaders and followers, and employs two different search methods to achieve steady convergence. However, the search methods adopted by SSA are relatively simple, hence SSA always trapped by local optima on complex multimodal problems. To improve the exploration of SSA, this paper proposes a multi-food source differential salp swarm algorithm (MFD-SSA) by transferring multi-leader mechanism and differential mutation strategy into SSA. MFD-SSA regards a portion of high fitness individuals as food sources and employ them for updating the position of leaders, the differential evolution mutation and the chaotic crossover are adopted to enhance swarm diversity. Then, the differential mutation and crossover operator are employed for enlarging search range. The MFD-SSA is tested on CEC2017 test suite and compared with five SSA variants and five other meta-heuristics. The test results indicate that MFD-SSA bears strong exploration, it outperforms five SSA variants and five meta-heuristics. On Planetary gear train design problem, MFD-SSA performs better than other five SSA variants too.

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ICCVIT '23: Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology
August 2023
378 pages
ISBN:9798400708701
DOI:10.1145/3627341
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 December 2023

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Applied Characteristic Disciplines of Electronic Science and Technology of Xiangnan University
  • Scientific Research Start-up Fund for High-level Talents in Xiangnan University
  • Scientific Research Fund of Hunan Provincial Education Department under Grant
  • Hunan Provincial Natural Science Foundation of China under Grant
  • Natural Science Foundation of Hunan Province

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ICCVIT 2023

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ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
Overall Acceptance Rate 54 of 142 submissions, 38%

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