A Nature-Inspired Metaheuristic Optimization Algorithm Based on Crocodiles Hunting Search (CHS)

Abstract

The increasing difficulty of actual-world optimization problems has prompted computer researchers to regularly produce additional process improvement techniques. Metaheuristic and evolutionary computing are very popular in nature-inspired optimization methods. This paper introduces the crocodile search algorithm (CHS), which is a revision of a new metaphorical algorithm based on the hunting behavior of crocodile herds. Various adaptive and arbitrary variables are combined within this algorithm to indicate the exploitation and investigation of the exploration area in various discoveries of optimization. The performance of the CHS is measured in different test phases. Initially, a collection of famous experiment events including unimodal, multi-modal, and composite functions are applied to examine exploitation, exploration, local optima avoidance, and convergence of CHS. The CHS algorithm achieves a regular frame for the airfoil with a pretty low drag, which explains that the methods can be efficient while working physical difficulties including restrained plus unknown search.