Title: A novel differential evolution with staged diversity enhancement strategy

Authors: Wei Li; Yafeng Sun; Ying Huang

Addresses: School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou City, Jiangxi Province, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou City, Jiangxi Province, China ' School of Mathematics and Computer Science, Gannan Normal University, Ganzhou City, Jiangxi Province, China

Abstract: Differential evolution (DE) algorithm is a simple and efficient evolutionary computing technology. Although DE has achieved good results in many fields, inappropriate parameter combinations can easily lead to the problem of premature convergence. In response to this problem, this paper proposed an effective DE with staged diversity enhancement strategy (SDESDE), which can increase the diversity of the population. In the early stage of SDESDE evolutionary process, SDESDE emphasises the balance search strategy, and use the diversity enhancement strategy to avoid getting trapped in the local optima in the middle stage. In the later stage, the faster convergence strategy is adopted. Besides, an adaptive mechanism is added to enhance the control of population diversity at different stages to close to the global optima faster and improve the efficiency of search. The proposed SDESDE algorithm is compared with four representative DE and experimental results demonstrate that the proposed algorithm not only has better performance in maintaining population diversity but also has highly competitive in overall performance.

Keywords: differential evolution; staged strategy; diversity enhancement; adaptive mechanism.

DOI: 10.1504/IJICA.2022.128435

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.5/6, pp.278 - 289

Received: 09 Sep 2020
Accepted: 23 Nov 2020
Published online: 23 Jan 2023 *

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