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Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems

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Abstract

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.

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Acknowledgements

This work was supported in part by the college-enterprise cooperation project of the domestic visiting engineer of colleges (FG2020077), Zhejiang, China, General research project of Zhejiang Provincial Education Department (Y201942618), Zhejiang, China, the National Natural Science Foundation of China (62076185, U1809209), the Beijing Natural Science Foundation (L182015), Zhejiang Provincial Natural Science Foundation of China (LY21F020030), Wenzhou Science & Technology Bureau (2018ZG016). We acknowledge comments of the reviewers.

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Author notes

  1. Guoxi Liang, Huiling Chen and Zhifang Pan authors contributed equally to this work.

Authors and Affiliations

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

    Hongliang Zhang, Ali Asghar Heidari & Huiling Chen

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Tong Liu

  3. Shanghai Lixin University of Accounting and Finance, Shanghai, 201209, China

    Xiaojia Ye

  4. Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China

    Guoxi Liang

  5. The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China

    Zhifang Pan

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  1. Hongliang Zhang
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Appendix A

Appendix A

See Tables 19, 20, 21, 22, 23, and 24.

Table 19 The comparison of the statistical results obtained by all the algorithms for IEEE CEC2014 benchmark set at D = 30
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Table 20 The comparison of the statistical results obtained by all the algorithms for IEEE CEC2014 benchmark set at D = 50
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Table 21 The comparison of the statistical results obtained by all the algorithms for IEEE CEC2014 benchmark set at D = 100
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Table 22 The statistics results of all the methods on the fitness value
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Table 23 The statistics results of all the methods on the error value
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Table 24 The statistics results of all the methods on the feature number
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Zhang, H., Liu, T., Ye, X. et al. Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Engineering with Computers 39, 1735–1769 (2023). https://doi.org/10.1007/s00366-021-01545-x

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