Study of Modified Particle Swarm Optimization Algorithm Based on Immune Clone Principle

A modified particle swarm optimization (PSO) algorithm was adopted to optimize functions, which overcame the shortcoming of converging to local optimum for PSO algorithm, increased the converging rate and achieve the global searching. PSO algorithm is a random optimizing algorithm based on swarm intelligence that has a simple parameter structure; however, it has a slow converging rate and is easy to obtain a local optimum. Through a considerate analysis of PSO algorithm, immunity clone (IC) algorithm was introduced to the PSO algorithm based on traditional velocity-displacement operator. The antibodies could be regarded as the particles, and according to the degree of affinity, the clone selection, clone suppression, and high-frequency mutation were performed, which could enhance the diversity of particle swarms and the capability of global searching. From the test results, it is shown that this algorithm has perfect property in multi-dimension function searching and needs shorter searching time and fewer iteration times than PSO algorithm.