基于粒子群算法的改进遗传算法的研究.doc

文档介绍：
基于粒子群算法的改进遗传算法的研究#
陶磊,景晓军**
(北京邮电大学信息与通信工程学院,北京 100876)
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摘要:遗传算法是一种模拟生物进化论的智能优化算法,具有简单、通用、鲁棒性强等优
点。遗传算法自提出以来已成功应用到很多领域,但经典遗传算法存在易陷入局部极值、复
杂度较高的问题。为克服这一缺点,本文将粒子群算法的思想应用到遗传算法的计算过程
中并在交叉操作中引入欧氏距离,提出了一种基于粒子群算法的改进的遗传算法。对标准
测试函数的实验表明,本文提出的算法较经典遗传算法和粒子群算法,具有更好的全局搜
索能力,同时也具有更强的局部搜索能力。
关键词:遗传算法;粒子群算法;局部极值;欧氏距离
中图分类号:
Improved ic Algorithm Based on Particle Swarm
Algorithm
TAO Lei, JING Xiaojun
(School of information munication engineering, Beijing university of posts and
munications, Beijing 100876)
Abstract: ic algorithm (GA) is a search heuristic that mimics the process of natural selection
and GA is used to generate useful solutions to optimization and search problems. In older to
e premature convergence and local optimization in simple ic algorithm, this paper
proposed an improved ic algorithm–particle swarm optimization (GA-PSO) algorithm. The
improved GA-PSO algorithm avoids obtaining local solution with applying PSO and Euclidian
data distance to mutation procedure on GA’s differentiation. Experiments proved that the
proposed algorithm can greatly improve the global convergence ability and enhance the rate of
convergence.
Key words: ic algorithm; Particle swarm algorithm; Local extreme; Euclidean distance
0 引言
遗传算法(ic Algorithm, GA)的概念是上世纪 60 年代由美国密执安大学的 Holland
教授首次提出的[1][2],其基本过程是:用问题解空间中的点来模拟生物体,用适应度函数评
估生物体对