摘要：An improved particle swarm optimizer (IPSO) with artificial immune algorithm (AIA) is proposed based on basic particle swarm optimization (BPSO). IPSO which is divided into two phases during the evolutionary process is different from BPSO. AIA remaining the diversity of population is applied in the first phase. Sub-population is formed by the optimum values sorted near the top from the first phase. Some sub-population evaluate at the same time to improve the performance of local convergence and get the global optimum value. Most Benchmark function get good result with IPSO which ability of optimization is better than BPSO.
