| 基于RBF神经网络与遗传算法的Agent初始位置优化 |
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| 引用本文： | 吴春国,梁艳春,葛宏伟. 基于RBF神经网络与遗传算法的Agent初始位置优化[J]. 吉林大学学报(信息科学版), 2003, 21(4): 382-386 |
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| 作者姓名： | 吴春国 梁艳春 葛宏伟 |
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| 作者单位： | 1. 吉林大学,计算机科学与技术学院,国家教育部符号计算与知识工程重点实验室,吉林,长春,130012
2. 吉林大学,数学学院,吉林,长春,130012 |
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| 基金项目： | 吉林省科技发展计划基金资助项目(20030520)，教育部科学技术研究重点基金资助项目(02090) |
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| 摘 要： | 以陆地作战训练模型为背景，研究了多Agent系统中Agent初始属性的优化问题，提出了一种径向基函数(RBF：Radial Basis Function)神经网络与遗传算法(GA：Genetic Alorithm)相结合的、对作战训练模型中Agent的初始位置进行优化的方法。与已有的优化方法相比，该方法不仅优化效果得到明显的提高，而且执行效率可以提高20余倍，更适于处理对执行效率要求较高的优化问题。
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| 关 键 词： | 多Agent系统 径向基函数神经网络 支持向量机 遗传算法 |
| 文章编号： | 1671-5896(2003)04-0382-05 |
| 修稿时间： | 2002-12-16 |
Optimization of initial positions of agents based on RBF neural network and genetic algorithm |
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| WU Chun|guo+,LIANG Yan|chun+,GE Hong|wei+ ry of Symbol Computation and Knowledge Engineering of the Ministry of Education,Changchun ,China, ^College of Mathematics Science,Jilin University,Changchun ,China). Optimization of initial positions of agents based on RBF neural network and genetic algorithm[J]. Journal of Jilin University:Information Sci Ed, 2003, 21(4): 382-386 |
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| Authors: | WU Chun|guo+ LIANG Yan|chun+ GE Hong|wei+ ry of Symbol Computation Knowledge Engineering of the Ministry of Education Changchun China ^College of Mathematics Science Jilin University Changchun China) |
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| Affiliation: | WU Chun|guo+1,LIANG Yan|chun+1,GE Hong|wei+2 ry of Symbol Computation and Knowledge Engineering of the Ministry of Education,Changchun 130012,China, 2^College of Mathematics Science,Jilin University,Changchun 130012,China) |
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| Abstract: | The initial properties of agents in the MAS(Multi|Agent System) on the background of land combat simulation model are studied. It proposes a method which combines the RBF(Radial Basis Function) neural network and GA(Genetic Algorithm) to optimize the initial positions of agents in the land combat simulation model. The comparison with the existing method shows that the optimizing results of the proposed method can be increased obviously and the efficiency of the performance can be increased more than twenty times. Therefore, this method is more suitable to handle optimization problems with requirement of speed|up time. |
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| Keywords: | Multi|agent system Radial basis function(RBF) neural network Support vector machine Genetic algorithm |
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