计算机工程 ›› 2012, Vol. 38 ›› Issue (20): 144-147.doi: 10.3969/j.issn.1000-3428.2012.20.037

• 人工智能及识别技术 • 上一篇    下一篇

基于自适应混沌粒子群优化的防空目标分配

王 毅 1a,赵建军 1b,冯巍巍 2,付龙文 2,陈令新 2   

  1. (1. 海军航空工程学院 a. 研究生管理大队;b. 兵器科学与技术系,山东 烟台 264001; 2. 中国科学院烟台海岸带研究所海岸带环境过程重点实验室,山东 烟台 264003)
  • 收稿日期:2011-10-19 修回日期:2012-02-08 出版日期:2012-10-20 发布日期:2012-10-17
  • 作者简介:王 毅(1983-),男,博士研究生,主研方向:人工智能,遗传算法;赵建军,教授;冯巍巍、付龙文,助理研究员;陈令新,研究员

Air Defense Target Distribution Based on Adaptive Chaotic Particle Swarm Optimization

WANG Yi 1 a, ZHAO Jian-jun 1 b, FENG Wei-wei  2, FU Long-wen 2, CHEN Ling-xin 2   

  1. (1a. Graduate Student’s Brigade; 1b. Department of Ordance Science and Technology, Naval Aeronautic and Astronautical University, Yantai 264001, China; 2. Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China)
  • Received:2011-10-19 Revised:2012-02-08 Online:2012-10-20 Published:2012-10-17

摘要: 粒子群优化算法存在早熟收敛和搜索精度较低的问题。为此,提出一种基于自适应混沌粒子群的优化算法。采用自适应权重和遗传算法中的交叉、变异操作更新粒子群,增加种群粒子的多样性,运用早熟判断机制判断粒子的当前状态,当粒子处于早熟状态时,利用混沌搜索的方法引导群体快速跳出局部最优。仿真结果表明,该算法可以有效解决粒子群算法的早熟问题,提高搜索精度和收敛速度。

关键词: 目标分配, 遗传算法, 自适应权重, 混沌, 粒子群优化算法

Abstract: An algorithm based on adaptive chaotic Particle Swarm Optimization(PSO) is put forward to overcome the problem of the premature convergence and low precision of PSO algorithm. This algorithm mixes adaptive weight, selection and variation operation of genetic algorithm and chaos algorithm. This can increase the species diversity of particles. The method of judging the local convergence is used to judge the particles statement. When the particles are in local convergence, the chaotic search method is used to guide the group out of local optima. Simulation results show that this algorithm not only can solve the local convergence problem effectively, but also can speed up the convergence rate and improve the search precision.

Key words: target distribution, Genetic Algorithm(GA), adaptive weight, chaos, Particle Swarm Optimization(PSO) algorithm

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