摘要
针对标准粒子群优化算法易陷入局部最优的缺点,提出了一种遗传粒子群混合算法。通过对算法中惰性粒子和局部最优粒子分别进行交叉变异,以及消除粒子速度对寻优的干扰,从而避免了粒子种群单一化和局部最优的问题。将该算法应用于虚拟企业伙伴选择实验,结果表明在进化代数和最优值方面是令人满意的。
For the standard particle swarm optimization algorithm is easy to fall into local extremum, this paper proposes a genetic and particle swarm optimization hybrid algorithm. The hybrid algorithm has avoided the problems of single par-ticle swam and local extremum by executing crossover and mutation operation to the lazy particles and the local optimum particles, eliminating the interference of the particle velocity to the optimization process. Finally, this algorithm has been applied to the experiment of virtual enterprise partner selection, the experiment show that it has satisfied results in evolu-tion generations and optimal value.
出处
《计算机工程与应用》
CSCD
2014年第1期58-62,共5页
Computer Engineering and Applications
基金
安徽省教育厅自然科学研究重点项目(No.2011A006)
关键词
粒子群优化算法
遗传算法
伙伴选择
particle swam optimization
genetic algorithm
partner selection
