Hybrid particle swarm optimizer with advance and retreat strategy and clonal mechanism for global numerical optimization

A novel particle swarm optimization algorithm based on advance and retreat strategy and clone mechanism (ARC-PSO) is proposed in this paper. It is well known that the advance-and-retreat strategy is a simple and effective method of one-dimensional search. We use the advance-and-retreat strategy to endow the clones with faster speed to find nearby local basins before next clonal operation. Furthermore, in the next clonal operation, the search space is enlarged greatly and the diversity of clones is increased. When the fitness value turns better after last ldquoflyingrdquo, the cloned particle advances. On the contrary, the cloned particle retreats then searches in the reverse direction of the last ldquoflyingrdquo with a small step-size of the previous velocity. Thus, the swarm has strong optimization ability. Comparisons among the proposed ARC-PSO, the conventional standard particle swarm optimization (SPSO) and the pure clone particle swarm optimization (CPSO) on thirteen benchmark test functions are presented in this paper. Experimental results show that the proposed ARC-PSO is capable of speeding up the evolution process significantly and improving the performance of global optimizer greatly.