Novel dynamic particle swarm optimizer algorithm
一种新型的动态粒子群优化算法

In order to improve the standard particle swarm optimization algorithm global search performance, a novel particle swarm optimization algorithm with population dynamics was proposed. When the algorithm search stagnation, the population was divided into two sub-populations. Population diversity was obtained by using random initialization particles and alternative mechanisms of sub-populations in the period of two sub-populations parallel searching. after sub-populations parallel searching, the information of particle in the different sub-population was exchange by mixing two sub-population into one population. The convergence of proposed algorithm is analyzed and the results indicate that it can guarantee converge on the global minimum. The functional test shows that proposed algorithm has better global search ability and fast convergence speed.