计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 48-51.

• 智能计算 • 上一篇    下一篇

带收缩与发散操作的自适应粒子群优化算法

赵志刚,尹兆远,林玉娇   

  1. 广西大学计算机与电子信息学院 南宁530004,广西大学计算机与电子信息学院 南宁530004,广西大学计算机与电子信息学院 南宁530004
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61363067)资助

Adaptive Particle Swarm Optimization Algorithm with Shrink and Expansion Operation

ZHAO Zhi-gang, YIN Zhao-yuan and LIN Yu-jiao   

  • Online:2018-11-14 Published:2018-11-14

摘要: 提出一种结合收缩与发散操作的自适应粒子群算法,其在运行过程中通过判断种群收敛情况与进化情况,自适应地选择粒子的运动行为。通过收缩操作使群体向极值点快速靠拢,通过发散操作保证粒子有能力跳出局部极值点,并根据种群进化状况在两种操作间转换。实验表明,该算法具有较强的跳出局部极值、逼近全局最优解的能力,在高维多峰函数上有较出色的表现。

Abstract: An adaptive particle swarm optimization algorithm with shrink and expansion operation was presneted,which can adaptively choose the behavior of the particle by detecting the degree of convergence during running.These two operations make particle swarm converge to extreme point and jump out of it quickly,and the evolutionary status of the population make them convert between these two operations adaptively .Experimental data show this algorithm has strong ability to get rid of the local optima and approach to global optima,especially in tackling the problem of high dimension multimodal function.

Key words: Particle swarm optimization,Shrink operation,Expansion operation,Adaptive detection

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