Abstract: A novel PSO-GA hybrid algorithm(PGHA) is proposed, combining the strengths of particle swarm optimization with genetic algorithm. PGHA combines the update rules of velocity and situation of PSO’s with the ideas of selection, crossover and mutation from GA.. Through comparing PGHA to standard PSO in evolving solution to two standard functions, results show that PGHA outperforms PSO.

Key words: Particle Swarm Optimization, Genetic Algorithm, PSO-GA Hybrid Algorithm

摘要： 结合PSO算法和GA算法的优势，提出了一种新颖的PSO-GA混合算法（PGHA）。混合算法利用了PSO算法的速率和位置的更新规则，并引入了GA算法里的选择、交叉和变异思想。通过混合算法对四个标准函数进行实验并与标准PSO算法比较，结果表明混合算法表现出更好的性能。

关键词: 微粒群算法, 遗传算法, PSO-GA混合算法