Title: Estimation of distribution algorithms based on increment clustering for multiple optima in dynamic environments

Authors: Bolin Yu; Zengkai Wang; Fahong Yu; Longhua Ma; Xiaoyun Xia; Feng He

Addresses: School of Electronics and Communication, Shenzhen Institute of Information Technology, Shenzhen, China ' College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, China ' College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, China ' Ningbo Institute of Technology, Zhejiang University, Ningbo, China ' College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, China ' College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, China

Abstract: Aiming to locate and track multiple optima in dynamic multimodal environments, an estimation of distribution algorithms based on increment clustering is proposed. The main idea of the proposed algorithm is to construct several probability models based on an increment clustering which improved performance for locating multiple local optima and contributed to find the global optimal solution quickly for dynamic multimodal problems. Meanwhile, a policy of diffusion search is introduced to enhance the diversity of the population in a guided fashion when the environment is changed. The policy uses both the current population information and the part history information of the optimal solutions available. Experimental studies on the moving peaks benchmark are carried out to evaluate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms from the literature. The results show that the proposed algorithm is effective for the function with moving optimum and can adapt to the dynamic environments rapidly.

Keywords: EDAs; dynamic multimodal problems; diffusion policy; incremental clustering.

DOI: 10.1504/IJCSE.2019.101886

International Journal of Computational Science and Engineering, 2019 Vol.19 No.4, pp.581 - 589

Received: 15 Jun 2016
Accepted: 05 Jan 2017

Published online: 27 Aug 2019 *

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