Adaptive Vaccine Extraction Immune Particle Swarm Optimization Algorithm

Optimal particle is regarded as effective information in process of vaccine extraction for immune particle swarm optimization algorithm, it falls into "local optimum" for complex function optimization, which influences convergence speed and convergence precision. With respect to this problem, a novel algorithm, KIPSO (KIPSO, K-means immune Particle Swarm Optimization), is proposed, which is used for extracting vaccine, identifying clustering center and its maximum neighborhood. Obtained the most superior individual characteristic vaccine set, and updated vaccine extraction by self-adaptive method, so that the algorithm improved convergence and adaptability. Influence of parameters initial value on performance of KIPSO algorithm and sensitivity of KIPSO on the initial solution is analyzed and a recommended value of related parameters is given. The simulation results show that the proposed method is effective for complex function optimization.