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Memetic Computing
Erschienen in: Memetic Computing 3/2021

09.08.2021 | Regular research paper

Adaptive chaotic spherical evolution algorithm

verfasst von: Lin Yang, Shangce Gao, Haichuan Yang, Zonghui Cai, Zhenyu Lei, Yuki Todo

Erschienen in: Memetic Computing | Ausgabe 3/2021

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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.
Vorheriger Artikel Large scale reservoir operation through integrated meta-heuristic approach
Nächster Artikel A decomposition-based evolutionary algorithm for scalable multi/many-objective optimization

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Metadaten
Titel
Adaptive chaotic spherical evolution algorithm
verfasst von
Lin Yang
Shangce Gao
Haichuan Yang
Zonghui Cai
Zhenyu Lei
Yuki Todo
Publikationsdatum
09.08.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 3/2021
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-021-00341-w

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