摘要
针对并行多任务环境下Agent联盟的生成问题,提出了基于多种群蚂蚁算法的Agent联盟生成策略.在该联盟生成策略中,种群内部蚂蚁相互合作,协调资源分配并完成相应任务;种群间蚂蚁进行资源竞争,协调解决并行多项任务间的资源冲突.同时,改进的信息素更新策略在综合考虑局部联盟收益和全局联盟收益的基础上提高了算法的全局搜索能力和生成联盟的质量.仿真实验结果表明,文中算法在多种典型条件下都能生成比现有算法更加高效的联盟结构.
A multi-colony ant colony optimization （MCACO） is proposed for the coalition generation problem in the parallel multi-task environment. In this algorithm, the ants from the same colony cooperate to accomplish a task by reasonably allocating the resources, while those from different colonies scramble the resources and resolve the resource conflict among multiple parallel tasks. Moreover, a specially-designed pheromone update rule is applied to enhance the global search ability of MCACO and improve the generation quality of agent coalition by taking the tradeoff between the local optimization benefit and the global one. Simulation results indicate that the proposed rithm helps to generate more effective coalition in multiple benchmark environments.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2008年第9期11-14,30,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
教育部新世纪优秀人才基金资助项目(NCET-05-0734)
广东省自然科学基金资助项目(04020079)
南京大学软件国家重点实验室开放基金资助项目(200603)
东南大学移动通信国家重点实验室开放基金资助项目(A200605)
广东省科技攻关项目(2005B10101010)
关键词
多AGENT系统
多任务
联盟生成
蚁群算法
muhi-Agent system
multiple task
coalition generation
ant colony algorithm
