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抯 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.
particle swarm optimization improve artificial immune network convergence precision the search of optimal solution
Hongzhong Tang Yewei Xiao Huixian Huang Xuefeng Guo
College of Information Engineering, Xiangtan University, Xiangtan, 411105,Hunan Province, China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
103-106
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）