Pareto-Based Self-organizing Migrating Algorithm Solving 100-Digit Challenge

Truong, Thanh Cong; Diep, Quoc Bao; Zelinka, Ivan; Šenkeřík, Roman

doi:10.1007/978-3-030-37838-7_2

Cited by 2 publications

References 15 publications

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Binary Chimp Optimization Algorithm (BChOA): a New Binary Meta-heuristic for Solving Optimization Problems

Wang¹,

Khishe²,

Kaveh

et al. 2021

Cogn Comput

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Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired by chimps' individual intelligence and sexual motivation in their group hunting. The preferable performance of ChOA has been approved among other well-known meta-heuristic algorithms. However, its continuous nature makes it unsuitable for solving binary problems. Therefore, this paper proposes a novel binary version of ChOA and attempts to prove that the transfer function is the most important part of binary algorithms. Therefore, four S-shaped and V-shaped transfer functions, as well as a novel binary approach, have been utilized to investigate the efficiency of binary ChOAs (BChOA) in terms of convergence speed and local minima avoidance. In this regard, forty-three unimodal, multimodal, and composite optimization functions and ten IEEE CEC06-2019 benchmark functions were utilized to evaluate the efficiency of BChOAs. Furthermore, to validate the performance of BChOAs, four newly proposed binary optimization algorithms were compared with eighteen novel stateof-the-art algorithms. The results indicate that both the novel binary approach and V-shaped transfer functions improve the efficiency of BChOAs in a statistically significant way.

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