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Adaptive chaotic spherical evolution algorithm

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Abstract

Nature-inspired metaheuristic algorithms are often based on the first-order difference hypercube search style to search for optimum solutions. In contrast, the spherical evolution algorithm (SE) employs a spherical search style. SE is very effective; however, there is still room for improvement. In this study, we added a chaotic local search (CLS) to the SE to improve its performance. This CLS uses information from several chaotic maps and records each instance of success. The recorded historical success information guides the CLS to choose the chaotic map for the next iteration. In our experiment, we compare the chaotic spherical evolution algorithm (CSE) with the original SE and other metaheuristic algorithms. The test set consists of 29 benchmark functions from the CEC2017 benchmark set and 22 real-world optimization problems from the CEC2011 set. Additionally, the new parameter introduced in the CSE has also been briefly discussed. Experimental results indicate that the proposed CSE significantly performs better than its competitors.

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Acknowledgements

This research was partially supported by JSPS KAKENHI Grant Number JP19K12136.

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Authors and Affiliations

  1. Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan

    Lin Yang, Shangce Gao, Haichuan Yang, Zonghui Cai & Zhenyu Lei

  2. Faculty of Electrical, Information and Communication Engineering, Kanazawa University, Ishikawa-ken, 920-1192, Japan

    Yuki Todo

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  1. Lin Yang
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  2. Shangce Gao
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  4. Zonghui Cai
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Correspondence to Shangce Gao.

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Yang, L., Gao, S., Yang, H. et al. Adaptive chaotic spherical evolution algorithm. Memetic Comp. 13, 383–411 (2021). https://doi.org/10.1007/s12293-021-00341-w

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