A Novel Particle Swarm Optimization Method Using Clonal Selection Algorithm
Hong Xing Lu · 2009
Particle swarm optimization, a nature-inspired evolutionary algorithm, has been successful in solving a wide range of real-value optimization problems. However, little attempts have been made to extend it to discrete problems. In this paper, a new particle swarm optimization method based on the clonal selection algorithm is proposed to avoid premature convergence and guarantee the diversity of the population. The experimental results show that the new algorithm not only has great advantage of convergence property over clonal selection algorithm and PSO, but also can avoid the premature convergence problem effectively.