东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (12): 1692-1695.DOI: -

• 论著 • 上一篇    下一篇

一种改进的粒子群优化算法

吴沛锋;高立群;邹德旋;依玉峰;   

  1. 东北大学信息科学与工程学院;徐州师范大学电气工程及自动化学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60674021)

An improved particle swarm optimization algorithm

Wu, Pei-Feng (1); Gao, Li-Qun (1); Zou, De-Xuan (2); Yi, Yu-Feng (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) School of Electrical Engineering and Automation, Xuzhou Normal University, Xuzhou 221116, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Wu, P.-F.
  • About author:-
  • Supported by:
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摘要： 提出一种改进的粒子群算法(EDAPSO).这种改进算法结合分布估计算法的探索能力和粒子群算法的开发能力.首先利用EDAPSO算法解决无约束的问题,并且比较EDAPSO算法与其他三种经典的粒子群算法的结果.无约束问题的实验结果表明:EDAPSO算法可以找到更好的解,并且稳定性更高.然后EDAPSO算法被用来解决含有13个单元的电力系统的负荷经济分配问题.实验结果表明:EDAPSO算法所获得的解比近期文献所报道的解好.

关键词: 粒子群算法, 分布估计算法, 无约束问题, 经济分配问题, 探索能力

Abstract: An improved particle swarm optimization (EDAPSO) algorithm is proposed. The improved algorithm integrates the exploration of estimation of distribution algorithm (EDA) and the exploitation of the particle swarm optimization (PSO) algorithms. The EDAPSO algorithm is applied to solve unconstrained optimization problems and the results of the EDAPSO algorithm are compared with the results of other three classical PSO algorithms. The experimental results for unconstrained optimization problems show that the EDAPSO may find better solutions and has higher numerical stability. The EDAPSO algorithm is then applied to solve the economic dispatch problems of power system with 13 units. Experimental results show that the solution obtained by the EDAPSO algorithm is better than that reported in recent literatures.

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