Abstract: To improve the PSO algorithm which is a new population based optimization algorithm against trapping into local minima，a hybrid PSO algorithm combing ant colony strategy with PSO（APSO） is presented.In APSO some potential evolution directions are constructed for each particle in PSO，at the same time a strategy is presented to choose which one may be the local best for PSO evolution process just like pheromone table in ant colony algorithm.It is shown by tested with well-known benchmark functions that APSO algorithms are better than PSO algorithms with linearly decreasing weight.

Key words: Particle Swarm Optimization（PSO）, ant colony algorithm, local searching

摘要： 针对粒子群算法应用于复杂函数优化时可能出现过早收敛于局部最优解的情况，提出了一种改进的算法。通过构造单个粒子的多个进化方向和类似于蚂蚁群算法信息素表的选择机制，保留了粒子的多种可能进化方向。提高了粒子间的多样性差异，从而改善算法能力。改进后的混合粒子群算法的性能优于带线性递减权重的粒子群算法。

关键词: 粒子群优化, 蚁群算法, 局部搜索