An Improved Hybrid Method Combining Gravitational Search Algorithm With Dynamic Multi Swarm Particle Swarm Optimization

Abstract

GSA is badly suffering from a slow convergence rate and poor local search ability when solving complex optimization problems. To solve this problem, a new hybrid population-based algorithm is proposed with the combination of dynamic multi swarm particle swarm optimization and gravitational search algorithm (GSADMSPSO). The proposed algorithm has divided the main population of masses into smaller sub-swarms and also stabilizing them by presenting a new neighborhood strategy. Then, by adopting the global search ability of the proposed algorithm, each agent (particle) improves the position and velocity. The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm. In order to evaluate the competences of the proposed algorithm, benchmark functions are employed. The experimental results have been confirmed a better performance of GSADMSPSO as compared with the other gravitational and PSO variants in terms of fitness rate.