An Improved Particle Swarm Optimization Algorithm Based on Immune System

An improved immune particle swarm optimization is proposed to solve drawbacks of low convergence rate and local optimization. The improved algorithm is to increase the diversity of the population of particles of PSO and to expand the search space of solutions by introducing the immune algorithm's antibody concentration regulation mechanisms and immune selection operation. It is to improve the convergence rate and precision of PSO by using immune memory and immune vaccine. According to the characteristics of specific problem, the improved algorithm defines a new selection operator of antibody concentration and immune vaccines to improve the optimizing ability of the algorithm. The proposed algorithm is applied to TSP problem and results show that the improved algorithm has an effective global optimization and higher convergence rate.