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Cumulative learning-based competitive swarm optimizer for large-scale optimization

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Abstract

Competitive swarm optimizer (CSO) has shown advantages for solving large-scale optimization. However, some major problems, such as low solution accuracy and slow exploration speed, are still not effectively solved. To alleviate these problems, this paper proposes an enhanced version of CSO (shorted for CLBCSO), which uses the cumulative learning mechanism to provide promising evolutionary direction and strengthen the exploitation ability of losers. Moreover, a multi-directional learning strategy is introduced to guide the losers to explore in different directions, which can significantly improve the exploration performance of the population. CEC2014 benchmark functions, time series prediction problems and classification problem are employed to evaluate the effectiveness of CLBCSO algorithm. Experimental validation shows that the average excellent rate of CLBCSO in solving 30 CEC2014 benchmark functions with 50 variables and 100 variables is 77.08% and 79.58%, respectively. This confirms that the proposed CLBCSO algorithm is competitive compared with three CSO optimizers and five popular optimization algorithms.

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Acknowledgements

This research is partly supported by the National Natural Science Foundation of China under Project Code (62176146, 61773314).

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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, Liangqilin Ni, Zhou Lei & Lei Wang

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

    Wei Li & Lei Wang

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  1. Wei Li
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  2. Liangqilin Ni
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  3. Zhou Lei
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  4. Lei Wang
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Correspondence to Wei Li.

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Appendix A: The information of the CEC2014 test suite

Appendix A: The information of the CEC2014 test suite

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Table 21 Information of the CEC2014 Test Functions
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21

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Li, W., Ni, L., Lei, Z. et al. Cumulative learning-based competitive swarm optimizer for large-scale optimization. J Supercomput 78, 17619–17656 (2022). https://doi.org/10.1007/s11227-022-04553-w

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