Mutation and memory mechanism for improving Glowworm Swarm Optimization algorithm

Abstract

The Glowworm Swarm Optimization (GSO) is a population-based metaheuristic algorithm for optimization problem. Disadvantages of GSO are low accuracy, convergence speed and weakness in the capability of global search which need to be improved. Thus, Memory Mechanism and Mutation Glowworm Swarm Optimization (MMGSO) are proposed in this study to find a solution for this problem. The proposed method is examined on Unimodal and Multimodal benchmark functions to enhance the GSO algorithm of solution quality, convergence speed and robustness. The results of MMGSO are analyzed and compared with GSO to prove the efficiency of the proposed method