Abstract: Since the standard Particle Swarm Optimization（PSO） algorithm is easy to fall into local optimum and converges slowly, a novel particle swarm optimization algorithm combined with the search operator of artificial bee colony algorithm and that of shuffled frog leaping algorithm is proposed. Firstly, use the search operator of artificial bee colony algorithm to improve the ability of explore to avoid falling into local optimum；Secondly, introduce the update operator of shuffled frog leaping algorithm into PSO to enhance the accuracy of convergence. Through the twelve standard test functions simulation experiments and compared with other algorithms, experiment results show that the proposed algorithm can avoid falling into local optimization and significantly improve convergence speed.

Key words: Particle Swarm Optimization（PSO）, artificial bee colony optimization, shuffled frog leaping algorithm

摘要： 由于标准粒子群算法易于陷入局部最优和收敛速度慢等问题，提出了一种引入人工蜂群搜索策略和混合蛙跳搜索策略的粒子群算法（ABCSFL-PSO）。使用人工蜂群的搜索策略提高算法的探索能力，避免算法陷入局部最优；使用蛙跳算法中更新最差粒子的策略，来加快算法收敛速度，并进一步提高求解精度。在12个标准测试函数上的仿真实验结果表明，算法性能优良，不仅能够避免陷入局部最优，而且显著提升了收敛速度。

关键词: 粒子群算法, 人工蜂群, 蛙跳算法