An enhanced chimp optimization algorithm for continuous optimization domains

Abstract Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to more flexible complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp (EChOA) improve accuracy solutions. First, highly disruptive polynomial mutation used initialize population, which provides foundation global search. Next, Spearman’s rank correlation coefficient chimps with lowest calculated respect leader chimp. To reduce probability falling into local optimum, beetle antennae operator less fit while gaining visual capability. Three strategies enhance exploration exploitation algorithm. verify function performance, EChOA comprehensively analyzed on 12 classical benchmark functions 15 CEC2017 functions. Besides, practicability also highlighted by three engineering design problems training multilayer perceptron. Compared ChOA five state-of-the-art algorithms, statistical results show that has strong competitive capabilities promising prospects.