Differential Evolution with Group-Based Competitive Control Parameter Setting for Numerical Optimization

Mengnan Tian, Yanghan Gao, Xingshi He (), Qingqing Zhang and Yanhui Meng
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Mengnan Tian: School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China
Yanghan Gao: School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China
Xingshi He: School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China
Qingqing Zhang: School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China
Yanhui Meng: School of Sciences, Xi’an Polytechnic University, Xi’an 710048, China

Mathematics, 2023, vol. 11, issue 15, 1-30

Abstract: Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named “DE/current-to-pbest_id/1”) and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer.

Keywords: numerical optimization; differential evolution; mutation strategy; control parameter setting; population reduction mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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