Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization

Jian Zhao (), Bochen Zhang, Xiwang Guo, Liang Qi and Zhiwu Li
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Jian Zhao: School of Science, University of Science and Technology Liaoning, Anshan 114051, China
Bochen Zhang: School of Science, University of Science and Technology Liaoning, Anshan 114051, China
Xiwang Guo: College of Computer and Communication Engineering, Liaoning Shihua University, Fushun 113001, China
Liang Qi: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Zhiwu Li: Institute of Systems Engineering, Macau University of Science and Technology, Macau 999087, China

Mathematics, 2022, vol. 10, issue 23, 1-31

Abstract: The spherical search algorithm is an effective optimizer to solve bound-constrained non-linear global optimization problems. Nevertheless, it may fall into the local optima when handling combination optimization problems. This paper proposes an enhanced self-adapting spherical search algorithm with differential evolution (SSDE), which is characterized by an opposition-based learning strategy, a staged search mechanism, a non-linear self-adapting parameter, and a mutation-crossover approach. To demonstrate the outstanding performance of the SSDE, eight optimizers on the CEC2017 benchmark problems are compared. In addition, two practical constrained engineering problems (the welded beam design problem and the pressure vessel design problem) are solved by the SSDE. Experimental results show that the proposed algorithm is highly competitive compared with state-of-the-art algorithms.

Keywords: differential evolution; opposition-based learning; spherical search algorithm; staged search mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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