| 基于智能体的多目标社会进化算法 |
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| 引用本文： | 潘晓英,刘 芳,焦李成.基于智能体的多目标社会进化算法[J].软件学报,2009,20(7):1703-1713. |
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| 作者姓名： | 潘晓英 刘 芳 焦李成 |
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| 作者单位： | 1. 西安电子科技大学,智能信息处理研究所,智能感知与图像理解教育部重点实验室,陕西,西安,710071
2. 西安电子科技大学,计算机学院,陕西,西安,710071 |
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| 基金项目： | Supported by the National Natural Science Foundation of China under Grant Nos.60703107, 60703108, 60703109, 60702062 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2006AA01Z107 (国家高技术研究发展计划(863)); the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20060701007(国家教育部博士点基金); the National Basic Research Program of China under Grant No.2006CB705700 (国家重点基础研究发展计划(973)); the Program for Cheung Kong Scholars and Innovative Research Team in University of Ministry of Education of China under Grant No.IRT0645 (国家教育部长江学者和创新团队支持计划); the Natural Science Foundation of Shaanxi Province of China under Grant No.2007F32 (陕西省自然科学基金) |
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| 摘 要： | 提出了一种基于智能体的多目标社会进化算法用以求解多目标优化问题(multiobjective optimization problems,简称MOPs),通过多智能体进化的思想来完成Pareto 解集的寻优过程.该方法定义可信任度来表示智能体间的历史活动信息,并据此确定智能体的邻域、控制智能体间的行为.针对多目标问题的特点,设计了3 个进化算子分别体现适者生存、弱肉强食、多样性原则以及自学习的特性.同时采用擂台赛法则构造Pareto 解的存储种群.仿真实验结果表明,该算法能够较好地收敛到Pareto 最优解集上,并且具有良好的多样性.另外,通过对智能体局部邻域环境建立方式的分析结果表明引入“关系网模型”可有效提高算法的收敛速度,并能在一定程度上提高解的质量.
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| 关 键 词： | 多目标优化 多智能体系统 关系网模型 可信任度 擂台赛法则 |
| 收稿时间： | 2007/11/25 0:00:00 |
| 修稿时间： | 4/2/2008 12:00:00 AM |
Multiobjective Social Evolutionary Algorithm Based on Multi-Agent |
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| PAN Xiao-Ying,LIU Fang,JIAO Li-Cheng.Multiobjective Social Evolutionary Algorithm Based on Multi-Agent[J].Journal of Software,2009,20(7):1703-1713. |
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| Authors: | PAN Xiao-Ying LIU Fang JIAO Li-Cheng |
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| Affiliation: | PAN Xiao-Ying1,LIU Fang2,JIAO Li-Cheng1 1 2 |
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| Abstract: | In this paper, a multi-agent social evolutionary algorithm is proposed for multiobjective optimization problems. It completes the search process by the agent evolution. MOMASEA (multi-agent social evolutionary algorithm for multiobjective) defines the trust degree to denote the historical information of agents, and the neighborhood of agent is confirmed by it. According to the characteristic of multiobjective problems, three evolutionary operators are designed to complete the whole evolutionary process. The experimental results show that MOMASEA has a good convergence to the Pareto set. Furthermore, the analysis of the mode for instructs local environment verified that importing acquaintance net model can speed up the convergence effectively. |
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| Keywords: | multiobjective optimization multi-agent system acquaintance net trust degree arena's principle |
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