Immune Particle Swarm Optimization with Diversity Monitoring
Particle swarm optimization is easy to trap into local optimum because the diversity of population becomes worse during the evolution. An immune particle swarm optimization with diversity monitoring (DIPSO) is proposed. The algorithm monitors diversity of population with diversity function. Clone selection operator and immune memory characteristic are introduced to update particles when diversity decline to some degree. The modified algorithm can avoid the local optimization and has better search performance for multi-peak functions. Testing over the benchmark problems,the experimental results show the modified algorithm has better convergence performance than original particle swarm optimization algorithm.