Performance Comparison of Evolutionary Biogeography Based Whale Optimizations with Chaotic Arc Adaptive Grasshopper Optimization Algorithms

Abstract

Whale Optimization Algorithm and Grasshopper Optimization Algorithm are two heuristic search methods inspired by the intelligence of whales and grasshoppers that are very useful for solving global optimization problems. The Evolutionary Biogeography-based Whale Optimization Algorithm was first introduced in the year 2020 to complete the global optimization function. Chaotic Arc Adaptive Grasshopper Optimization Algorithm is proposed in the year 2021 by integrating chaos sequence into the Grasshopper Optimization algorithm to improve its global search capability. The problem formulation of this research is which algorithm has the best performance of the two types of algorithms (EWOA, and AGOA). This paper aims to compare the performance between the Evolutionary Biogeography-based Whale Optimization Algorithm (EWOA) and the Chaotic Arc Adaptive Grasshopper Optimization Algorithm (AGOA). The performances of the Evolutionary Biogeography-based Whale Optimization Algorithm and Chaotic Arc Adaptive Grasshopper Optimization Algorithm are tested on four benchmark problems, namely Rotated Bent Cigar, Shifted and Rotated Rosenbrock, Shifted and Rotated Schwefel, and Shifted and Rotated Zakharov function. Based on the results, Evolutionary Biogeography-based Whale Optimization Algorithm is more efficient than the Chaotic Arc Adaptive Grasshopper Optimization Algorithm for solving standard test problems and engineering optimization problems.