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A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

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Abstract

The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

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Data availability statement

The data involved in this study are all public data, which can be downloaded through public channels.

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Acknowledgements

This research was supported by the Key R&D Program of Zhejiang (2022C03114), Zhejiang Provincial Natural Science Foundation of China (LJ19F020001, LZ22F020005), National Natural Science Foundation of China (62076185, U1809209), and Guangdong Natural Science Foundation (2021A1515011994).

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Authors and Affiliations

  1. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, Zhejiang, China

    Chao Lin, Ali Asghar Heidari & Huiling Chen

  2. College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China

    Pengjun Wang

  3. School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Xuehua Zhao

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Appendices

Appendix 1

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Table 15 Results of RCSSSA and different MAs on 30 benchmark functions
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15

Appendix 2

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Table 16 Results of RCSSSA and different SSA variants on 30 benchmark functions
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16

Appendix 3

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Table 17 Results of RCSSSA and various variants under 30 benchmark functions
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17

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Lin, C., Wang, P., Heidari, A.A. et al. A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis. J Bionic Eng 20, 1296–1332 (2023). https://doi.org/10.1007/s42235-022-00304-y

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