Immune-Particle Swarm Optimization Beats Genetic Algorithms

There exists the disadvantages such as prematurity in particle swarm optimization because of the decrease of swarm diversity. In order to solve this problem an immune particle swarm optimization(Immune-PSO)algorithm is proposed which is combined with immune clone selection algorithm, Clone copy operator, clone hyper-mutation operator and clone selection operator are performed during the evolutionary. Proportion clone copy according to particles´ affinity can protect eminent individuals and speed up convergence, clone hyper-mutation provides a new mechanism producing new ones and maintaining diversity clone selection which selects best individuals can avoid algorithm degenerate effective. The typical benchmark functions are performed. The numerical simulation results show that the improved algorithm not only can maintain swarm´s diversity speed up convergence speed but also help the algorithm escape from local extreme.