Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

Abstract

The Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are stochastic optimization techniques. The PSO is inspired by the simulated behavior of bird flocking whereas GSA is the physics-based heuristic technique that is inspired by the law of mass interaction. In this paper, a new hybridization algorithm CPSOGSA i.e. constriction coefficient based particle swarm optimization and gravitational search algorithm have been proposed. It combines the exploitation and exploration capabilities of PSO and GSA, respectively in order to obtain the best result. The experimental results on 23 standard unimodal and multimodal test functions confirm the better performance of CPSOGSA as compared with classical Particle swarm optimization and Gravitational search algorithm. The efficiency of the hybrid CPSOGSA has been demonstrated through faster intensification rate and avoidance from local minima.