[1]秦全德,李丽,程适,等.交互学习的粒子群优化算法[J].智能系统学报,2012,7(6):547-553.
　QIN Quande,LI Li,CHENG Shi,et al.Interactive learning particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2012,7(6):547-553.
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交互学习的粒子群优化算法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 7 期数: 2012年第6期 页码: 547-553 栏目: 学术论文—智能系统 出版日期: 2012-12-25
Title:
Interactive learning particle swarm optimization algorithm
文章编号:
1673-4785(2012)06-0547-07
作者:
秦全德1，李丽1，程适2,3，李荣钧4
1. 深圳大学 管理学院，广东 深圳 518060；
2. 英利利物浦大学 电气电子工程系，英国 利物浦 L69 3GJ；
3. 西交利物浦大学 电气电子工程系，江苏 苏州 215123;
4. 华南理工大学 工商管理学院, 广东 广州 510640
Author(s):
QIN Quande1, LI Li1, CHENG Shi2,3, LI Rongjun4
1. College of Management, Shenzhen University, Shenzhen 518060, China;
2. Department of Electrical Engineering and Electronics, Liverpool University, Liverpool L69 3GJ, UK;
3. Department of Electrical and Electronics Engineering, Xi’an JiaotongLiverpool University, Suzhou 215123, China;
4. School of Business Administration, South China University of Technology, Guangzhou 510640, China
关键词:
粒子群优化算法; 交互学习; 学习策略; 学习行为; 群体多样性
Keywords:
particle swarm optimization algorithm;  interactive learning;  learning strategy;  learning behavior;  population diversity
分类号:
TP18
文献标志码:
A
摘要:
分析基本的粒子群优化学习机制的缺陷，启发于人类社会不同群体之间可以交互学习的特点，提出了一种改进粒子群优化算法——ILPSO．在ILPSO算法中，粒子由2个种群构成．当2个种群中最佳的全局最优位置在连续一定的迭代次数内没有改善时，执行交互学习策略．依据每个种群的全局最优位置的适应值，运用模拟退火的机制和轮盘赌的方法确定学习种群和被学习种群．提出了一个基于适应度排序的经验公式，计算学习种群中的每个粒子向被学习种群学习的概率．为了摆脱选择压力，采用了一种速度变异的方法．多个测试函数的数值实验结果表明，ILPSO具有较好的全局搜索能力，是一种求解复杂问题的有效方法．
Abstract:
Analyzing the drawbacks of learning mechanism in the basic particle swarm optimization (PSO), an interactive learning particle swarm optimization (ILPSO) is presented, which is inspired by the phenomenon in human society that individuals in different groups can learn each other. Particles are composed of two populations in ILPSO. When the best particle’s fitness value of two populations does not improve within a certain number of successive iterations, interactive learning strategies are implemented. According to the best particle′s fitness value of each population, a simulated annealing mechanism and roulette method are used to identify the learning population and the learned population. This paper proposes an empirical formula of sorting fitness value to calculate the probability of each particle in the learning population learning from the learned population. In order to escape selection pressure, a speed mutation method is used. The numerical experimental results of some benchmark functions show that ILPSO has good global search capability and is an effective method for solving complicated problems.
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备注/Memo

收稿日期： 2012-05-07.
网络出版日期：2012-11-16.
基金项目：国家自然科学基金资助项目（71071057，71001072）；广东省自然科学基金资助项目（S2011010001337）.
通信作者：秦全德.
E-mail：qinquande@gmail.com.
作者简介：
秦全德，男，1979年生，讲师，博士，主要研究方向为智能计算及其应用、管理决策与优化等发表学术论文10余篇．

更新日期/Last Update: 2013-03-19
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