Evolutionary Multimodal Optimization Based on Bi-Population and Multi-Mutation Differential Evolution

Authors

Wei Li^{1, 2, *,} , Yaochi Fan¹, Qingzheng Xu³

¹School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China

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

³College of Information and Communication, National University of Defense Technology, Xi’an 710106, China

^*Corresponding author. Email: liwei@xaut.edu.cn

DOI

10.2991/ijcis.d.200826.001How to use a DOI?

Keywords

Differential evolution; Multi-mutation strategy; Fitness Euclidean-distance ratio; Multimodal optimization problems

Abstract

The most critical issue of multimodal evolutionary algorithms (EAs) is to find multiple distinct global optimal solutions in a run. EAs have been considered as suitable tools for multimodal optimization because of their population-based structure. However, EAs tend to converge toward one of the optimal solutions due to the difficulty of population diversity preservation. In this paper, we propose a bi-population and multi-mutation differential evolution (BMDE) algorithm for multimodal optimization problems. The novelties and contribution of BMDE include the following three aspects: First, bi-population evolution strategy is employed to perform multimodal optimization in parallel. The difference between inferior solutions and the current population can be considered as a promising direction toward the optimum. Second, multi-mutation strategy is introduced to balance exploration and exploitation in generating offspring. Third, the update strategy is applied to individuals with high similarity, which can improve the population diversity. Experimental results on CEC2013 benchmark problems show that the proposed BMDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms in terms of the quantity and quality of the optimal solutions.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Wei Li
AU  - Yaochi Fan
AU  - Qingzheng Xu
PY  - 2020
DA  - 2020/09/11
TI  - Evolutionary Multimodal Optimization Based on Bi-Population and Multi-Mutation Differential Evolution
JO  - International Journal of Computational Intelligence Systems
SP  - 1345
EP  - 1367
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200826.001
DO  - 10.2991/ijcis.d.200826.001
ID  - Li2020
ER  -