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Article type: Research Article
Authors: Yu, Xiaobinga; b; * | Liu, Zhenjiea | Wu, XueJinga | Wang, Xuminga
Affiliations: [a] School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China | [b] Key Laboratory of Meteorological Disaster (KLME), Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, China
Correspondence: [*] Corresponding author. Xiaobing Yu, E-mail: yuxb111@163.com.
Abstract: Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient.
Keywords: Differential evolution, simulated annealing, ranking, mutation operator, selection operator
DOI: 10.3233/JIFS-210239
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1375-1391, 2021
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