A hybrid Niching-based evolutionary PSO for numerical optimization problems

Particle swarm optimization (PSO) is a population-based optimization algorithm which has great potential because of its simplicity and malleability. PSO is a typical global searching heuristic, but there is still an insufficiency in PSO regarding solution exploitation and diversity. In view of this, inspired by the pseudo bacterial genetic algorithm (PBGA), we enhance the variety of solution exploitation by incorporating the PBGA process-chromosome mutation. In addition to this, a modified niching method is utilized to preserve the solution diversity, and to avoid premature convergence in search process. We call the proposed algorithm Niching-based Evolutionary PSO (NEPSO). The experimental results test several commonly used numerical benchmark functions, and show that NEPSO has very promising optimization performance.