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Hybridizing multi-objective, clustering and particle swarm optimization for multimodal optimization

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Abstract

Multimodal optimization problems (MMOPs) are a kind of common optimization problem aiming to find multiple high-accurate optimal solutions. In this paper, a multimodal optimization algorithm named MO-C-PSO is proposed. In the proposed method, we combine the exploration ability in the whole search space of multi-objective technique with the special clustering ability of mean-shift clustering method. In this way, each potential optimal individual is induced to form its own unique sub-population. Then, particle swarm optimization (PSO) guides the local search in each sub-population by applying the proposed switching evolutionary process (SEP) strategy, which can refine the solution accuracy. To evaluate the performance, the proposed method is compared with other state-of-the-art methods on CEC’2013 benchmark set, the classic high-dimensional problems, and a real-world application. The experimental results have validated that MO-C-PSO can provide competitive performance.

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China (61773106).

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Authors and Affiliations

  1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

    Tianzi Zheng, Jianchang Liu, Yuanchao Liu & Shubin Tan

  2. College of Information Science and Engineering, Northeastern University, Shenyang, China

    Tianzi Zheng, Jianchang Liu, Yuanchao Liu & Shubin Tan

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  1. Tianzi Zheng
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Correspondence to Jianchang Liu.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. The work is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. The manuscript is approved by all authors for publication.

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Zheng, T., Liu, J., Liu, Y. et al. Hybridizing multi-objective, clustering and particle swarm optimization for multimodal optimization. Neural Comput & Applic 34, 2247–2274 (2022). https://doi.org/10.1007/s00521-021-06355-2

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