[1]蒋建国,吴 琼,夏 娜.自适应粒子群算法求解Agent联盟[J].智能系统学报,2007,2(02):69-73.
 JIANG Jian-guo,WU Qiong,XIA Na.Solving Agent coalition using adaptive particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2007,2(02):69-73.
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自适应粒子群算法求解Agent联盟(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第2卷
期数:
2007年02期
页码:
69-73
栏目:
出版日期:
2007-04-25

文章信息/Info

Title:
Solving Agent coalition using adaptive particle swarm optimization algorithm
文章编号:
1673-4785(2007)02-0069-05
作者:
蒋建国吴 琼夏 娜
合肥工业大学计算机与信息学院 安徽 合肥 230009
Author(s):
JIANG Jian-guoWU QiongXIA Na
College of Computer and Information, Hefei University of Technology, Hefei 2300 09, China
关键词:
多Agent系统粒子群优化算法自适应粒子群算法联盟
Keywords:
MAS PSO algorithm adaptive particle swarm optimizationcoalition
分类号:
TP18
文献标志码:
A
摘要:
联盟生成是多Agent系统的一个关键问题,主要研究如何在多Agent系统中动态生成面向任务的最优Agent联盟.引入粒子群算法来解决这一问题,受到惯性权重c0在进化过程中所起作用的启发,引入自适应惯性权重cadp对粒子群算法进行改进,使其不再易于陷入局部极小.对比实验结果表明,该算法在解的性能和收敛速度上均优于相关算法.
Abstract:
Coalition Generation is a key issue in a MultiAgent System which prim arily focuses on generation of an optimal taskoriented coalition in a dynamic m anner. A particle swarm optimization (PSO) algorithm is adopted to solve the pro blem. And a novel “adaptive inertia weight” is proposed to improve PSO b y the illumination of function of inertia weight ,so as to avoid falling into lo cal minimum. The results of comparison experiments show that this algorithm is s uperior to other related methods in both performance of solution and convergence rate.

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备注/Memo

备注/Memo:
收稿日期:2006-10-12.
基金项目:国家自然科学基金资助项目(60474035)
作者简介:
蒋建国,男,1955年生,教授,博士生导师,合肥工业大学计算机与信息学院院长,国家政府津贴获得者,安徽省跨世纪学术带头人培养对象,全国信息与电子学科研究生教育委员会理事,安徽省计算机学会副理事长,主要研究方向为智能信息处理、分布式智能系统、数字图像分析与处理.发表论文30余篇,出版专著1部. E-mail: jgjiang@hfut.edu.cn.
吴 琼,女,1983年生,硕士研究生,主要研究方向为智能计算. 
夏 娜,男,1979年生,副教授,主要研究方向为分布式人工智能、智能控制等.
更新日期/Last Update: 2009-05-06