| 基于量子多目标进化算法的多任务Agent联盟生成 |
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| 引用本文： | 许波,彭志平,余建平,王永. 基于量子多目标进化算法的多任务Agent联盟生成[J]. 系统工程理论与实践, 2012, 32(10): 2253-2261. DOI: 10.12011/1000-6788(2012)10-2253 |
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| 作者姓名： | 许波 彭志平 余建平 王永 |
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| 作者单位： | 1. 广东石油化工学院 计算机科学与技术系, 茂名 525000;2. 湖南师范大学 数学与计算机科学学院, 长沙 410081;3. 湖南大学 电气与信息工程学院, 长沙 410082 |
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| 基金项目： | 广东省自然科学基金(10252500002000001);广东省教育部产学研结合项目(2010B090400235);湖南省教育厅优秀青年项目(10B062);国家自然科学基金(60903168) |
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| 摘 要： | 多Agent联盟生成是多Agent系统的关键问题之一, 主要研究如何在多Agent系统中动态生成面向任务的最优联盟. 为使Agent能稳定的组织起来完成单Agent不能完成的任务并在成本、资源、利益等方面达到一个良好的平衡性能并达到全局最优, 提出了联盟多目标综合评价模型, 并将量子进化多目标算法应用于多目标多任务Agent联盟问题, 运用编码的映射, 将资源组合和任务分配合并为一个过程, 降低了问题的复杂性. 对比实验结果表明该算法求得的解的质量高, 平衡性好, 能有效避免了联盟死锁和资源浪费.
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| 关 键 词： | 多Agent 联盟生成 多目标优化 量子多目标进化算法 |
| 收稿时间： | 2010-06-17 |
Multi-tasking agent coalition formation based on quantum multi-objective evolutionary algorithm |
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| XU Bo,PENG Zhi-ping,YU Jian-ping,WANG Yong. Multi-tasking agent coalition formation based on quantum multi-objective evolutionary algorithm[J]. Systems Engineering —Theory & Practice, 2012, 32(10): 2253-2261. DOI: 10.12011/1000-6788(2012)10-2253 |
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| Authors: | XU Bo PENG Zhi-ping YU Jian-ping WANG Yong |
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| Affiliation: | 1. Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Maoming 525000, China;2. College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China;3. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
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| Abstract: | Multi-agent coalition formation is one of the key problems in multi-agent systems.The main research is how to dynamically generate task-oriented coalition and the optimal structure in multi-agent systems.For the establishment of a mechanism to organize agent stability completes the task that can not be completed by a single agent and reach the global optimum.This paper presents a multi-objective comprehensive evaluation model,and a multi-objective quantum evolutionary algorithm is proposed to solve multi-agent coalition formation problem.The algorithm uses coding mapping,the combination of resources and task allocation are combined to a process,which reducing the complexity of the problem. Experimental results show that the proposed algorithm can solve the multi-task agent coalition formation problem effectively and efficiently. |
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| Keywords: | multi-agent coalition formation multi-objective optimization quantum multi-objective evolutionary algorithm |
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