Enhancing Genetic Algorithm Performance with Hybrid Strategy for Solving Optimization Problems

Abstract

This study presents a novel algorithm called hybrid-GA, which combines genetic algorithm (GA) with the Harris Hawks Optimization (HHO) algorithm to address the challenge of enhancing GA performance in solving optimization problems. While GA is known for its strong exploration capabilities, it often faces challenges in exploitation, limiting its ability to find global optimal solutions. The hybrid-GA algorithm aims to surpass existing methods by achieving a better balance between exploration and exploitation, resulting in improved solution quality, faster convergence, and enhanced exploration-exploitation ability. The algorithm effectiveness is demonstrated through experiments on six benchmark functions from CEC2017, where the hybrid-GA outperforms compared algorithms on five of six functions, showcasing its potential for enhancing GA performance in optimization problem-solving. These findings contribute to advancing the field by providing a promising solution to address the exploration-exploitation challenge in GA-based optimization.