A New Bio-Inspired Algorithm Based on the Hunting Behavior of Cheetah

Abstract

Soft computing is recognized as the fusion of methodologies mainly designed to model and formulate solutions to real-world problems that are too difficult to model mathematically. The grey wolf optimizer (GWO) algorithm is the recently proposed bio-inspired optimization algorithm that is mainly based on their foraging and hunting behavior. This GWO is proved as the recent and best in solving complex problems, but they too face some drawbacks of low solving precision, slow convergence, and bad local searching ability. In order to overcome the shortcomings of the existing algorithms, this paper is intended to propose a novel algorithm based on the foraging behavior of the cheetah. The cheetah is well known for their leadership hierarchy, decision making, and efficient communication capabilities between their teammates during group hunting. The famous benchmark functions such as unimodal and multimodal functions are being chosen as the testbed, and the experiments are performed on them. The proposed scheme outperforms in terms of computational time and optimal solution.