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

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 state-of-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.

Automated summary

The Chimp Optimization Algorithm (ChOA) is a new meta-heuristic algorithm inspired by individual intelligence and sexual motivation in chimps, showing better performance among other well-known algorithms. A binary version of ChOA was proposed in this study, emphasizing the importance of transfer functions for binary algorithms. Results indicate that the novel binary approach and V-shaped transfer functions significantly improve the efficiency of BChOAs.