Title: Differential evolution with spatially neighbourhood best search in dynamic environment

Authors: Dingcai Shen; Longyin Zhu

Addresses: Key Laboratory of Jiangxi Province for Numerical, Simulation and Emulation Techniques, Gannan Normal University, Ganzhou 341000, China; School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China ' College of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China

Abstract: In recent years, there has been a growing interest in applying differential evolution (DE) to optimisation problems in a dynamic environment. The ability of tracking a changing optimum over time is concerned in dynamic optimisation problems (DOPs). In this study, an improved niching-based scheme named spatially neighbourhood best search DE (SnDE) for DOPs is proposed. The SnDE adopts DE with DE/best/1/bin scheme. The best individual in the selected scheme is searched around the considered individual in a predefined neighbourhood size, thus keeping a balance between exploitation ability and exploration ability. A comparative study with several algorithms with different characteristics on a common platform by using the moving peaks benchmark (MPB) and various problem settings are presented in this study. The results indicate that the proposed algorithm can track the changing optimum in each circumstance effectively on the selected benchmark function.

Keywords: differential evolution; evolutionary algorithms; dynamic optimisation problem; DOP; neighbourhood search; niching.

DOI: 10.1504/IJCSE.2019.099644

International Journal of Computational Science and Engineering, 2019 Vol.19 No.1, pp.104 - 111

Received: 10 May 2016
Accepted: 13 Nov 2016

Published online: 20 May 2019 *

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