期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：1
页码：225-236
DOI：10.14257/ijgdc.2016.9.1.23
出版社：SERSC
摘要：Based on particle swarm optimization (PSO) algorithm and its power system reactive power optimization method to in-depth study and research proposed a new hybrid particle swarm optimization algorithm (HPSO). Algorithm combines the differential evolution algorithm and simulated annealing algorithm and particle swarm optimization algorithm, in particle searching optimal except for tracking individual and global, and tracks produced by particle information difference of the three value. At the same time, when the particle search space of one dimension speed lower than the setting value will be re initialized the dimensional particle velocity and the particle of differential evolution mutation. For the crossover and mutation operations, new solution may be worse than the original solution to, the introduction of simulated annealing algorithm, the metropolis rule in a certain extent accept bad solutions, allows the target function in a certain degree of deterioration, practical calculation is not according to the probability to choose the poor solution, but rather the judgment target function difference is less than allows the target function deterioration range. Hybrid particle swarm optimization algorithm combines the advantages of particle swarm optimization algorithm, differential evolution algorithm and simulated annealing algorithm, to maintain the diversity of particles, has very strong practicability.
关键词：Particle swarm optimization; power communication network; simulated ; annealing; differential evolution total quantity of knowledge
