关键词: 粒子群优化算法, 遗传算法, 伙伴选择

Abstract: 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 particle 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 evolution generations and optimal value.

Key words: particle swam optimization, genetic algorithm, partner selection