A Novel Dynamic Particle Swarm Optimization Algorithm Based on Improved Artificial Immune Network

To resolve the problem of the premature and low precision of the common particles swarm optimization (CPSO), the paper presents a novel dynamic particle swarm optimization algorithm based on improved artificial immune network (IAINPSO). Based on the variance of the population's fitness, a kind of convergence factor is adopted in order to adjust the ability of search. It is an effective way to combine with linear decreasing inertia weight. To enhance the performance of the local search ability and the search precision of the new algorithm, the improved artificial immune network is introduced in this paper. The experimental results show that the new algorithm has not only satisfied convergence precision, but also the number of iterations is much less than traditional scheme, and has much faster convergent speed, with excellent performance of in the search of optimal solution to multidimensional function.