[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(2):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(2):69-73.
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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
2
期数:
2007年第2期
页码:
69-73
栏目:
学术论文—人工智能基础
出版日期:
2007-04-25
- 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):
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JIANG Jian-guo,WU Qiong,XIA Na
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College of Computer and Information, Hefei University of Technology, Hefei 2300 09, China
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- 关键词:
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多Agent系统; 粒子群优化算法; 自适应粒子群算法; 联盟
- Keywords:
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MAS; PSO algorithm; adaptive particle swarm optimization; coalition
- 分类号:
-
TP18
- 文献标志码:
-
A
- 摘要:
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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c0在进化过程中所起作用的启发,引入自适应惯性权重cadp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:
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Coalition Generation is a key issue in a MultiAgent System which prim arily focuses on generation of an optimal taskoriented 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.
更新日期/Last Update:
2009-05-06