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An Improved Competitive Swarm Optimizer with Super-Particle-Leading

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Abstract

Competitive swarm optimizer (CSO) has been concerned in recent years due to its achievements in solving global optimization problems. However, the CSO algorithm still suffers from issues such as low solution precision and premature convergence since it only relies on the winners to guide the population evolution. To address this issue, an improved competitive swarm optimizer with super-particle-leading is proposed in this paper. First, the super particle obtained by the cumulative learning strategy is used to provide a promising evolution direction for the population. Next, the weight-based dynamic omnidirectional strategy is employed to enhance the population exploration ability. Finally, CEC2017 benchmark problems are used to evaluate the efficiency of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is competitive with the contender algorithms due to its better balance between exploration and exploitation.

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The experimental data used to support the findings of this study are included within the manuscript.

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Acknowledgements

This research is partly supported by the National Natural Science Foundation of China under Project Code (62176146, 61773314), and the Special project of Education Department of Shaanxi Provincial Government for Local Services (Program No. 21JC026).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wei Li, Yetong Gao & Lei Wang

  2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an, 710048, China

    Lei Wang

Authors
  1. Wei Li
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  2. Yetong Gao
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  3. Lei Wang
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Contributions

W.L. wrote the main manuscript text, Y.G. prepared figures 1-12, and L.W. prepared tables 1-8. All authors reviewed the manuscript.

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Correspondence to Wei Li.

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Li, W., Gao, Y. & Wang, L. An Improved Competitive Swarm Optimizer with Super-Particle-Leading. Neural Process Lett 55, 10501–10533 (2023). https://doi.org/10.1007/s11063-023-11336-8

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