Gbest-guided Imperialist Competitive Algorithm for Global Numerical Optimization

Abstract

Imperialist competitive(IC) optimization algorithm invented recently by Atashpaz Gargari is a heuristics algorithm. However, there is still an insufficiency in IC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by Particle swarm optimization, we propose an improved IC algorithm called gbest-guided IC (GIC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GIC algorithm can outperform IC algorithm in most of the experiments.