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基于蚁群算法的MAS多目标协调优化 被引量:8

Multi-objective coordinated optimal of MAS based on ant system
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摘要 利用蚁群算法的群体搜索策略,研究了基于蚁群算法的MAS多目标协调优化机制.对每个Agent的目标函数分配一群蚂蚁,使之在问题空间寻优,并对所有的优化解采用谈判机制进行协调,以产生多目标优化问题的Pareto折衷解.采用"误差率"和"空间矩阵"方法对算法的性能指标进行度量.用该方法求解两个典型的多目标优化测试函数,仿真结果表明所提出的方法可成功地解决MAS的多个目标函数的优化问题,收敛速度较快. A mechanism of multiple objective coordinated optimization based on ant system for MAS is proposed by using colony searching strategy. A family of ants is assigned for each objective of each agent, by which the optimal solution is searched in solution space. Negotiation mechanism is applied to coordinate all the solutions. Performance is measured by usihg "error ratio" and "spacing" metrics. Multiple objective coordinated optimization based on ant system is applied to two typical multiple objective test functions. Simulation results show that this algorithm is able to solve the multiple objective optimization problems successfully and has fast convergence speed.
出处 《控制与决策》 EI CSCD 北大核心 2007年第8期946-950,共5页 Control and Decision
基金 国家自然科学基金重点项目(60534040)
关键词 蚁群算法 多目标协调优化 谈判机制 Ant system Multiple-objective coordinated optimization Negotiation mechanism
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参考文献9

  • 1Mariano C E,Morales E F.Distributed reinforcement learning for multiple objective optimization problems[C].Evolutionary Computation 2000,Proc of 2000 Congress.California,2000,1:188-195.
  • 2汪镭,吴启迪.蚁群算法在连续空间寻优问题求解中的应用[J].控制与决策,2003,18(1):45-48. 被引量:100
  • 3士昌.蚁群算法及其在连续性空间优化问题中的应用[D].杭州:浙江大学,2002.
  • 4Mariano C E,Morales E F.(1999a)MOAQ an ant-Q algorithm for multiple objective optimization problems[C].Proc of the Genetic and Evolutionary Computation Conf.San Fancisco,1999:894-901.
  • 5Van Veldhuizen D,Lamont G.Multiobjective evolutionary algorithms test suites[C].Proc of Symposium Applied Computing.San Antonio,1999:351-357.
  • 6Schott J.Fault tolerant design using simple and multicriteria genetic algorithms[D].Cambridge:Department of Aeronautics and Astronautics,Massachusetts Institute of Technology,1995.
  • 7Deb K.Multiobjective genetic algorithms:Problem difficulties and construction of test problems[R].Dortmund:University of Dortmund,Department of Computer Science/XI,1998.
  • 8Srinivas N,Deb K.Multiobjective optimization using nondominated sorting in genetic algorithms[J].Evolutionary Computation,1994,2(3):221-248.
  • 9Van Veldhuizen D.Evolutionary algorithms:Classification,analysis and new innovations[D].Ohio:Air Force Institute of Technology,1999.

二级参考文献11

  • 1[1]Dorigo M, Gambardella L M. Ant colony system: A cooperative learning approach to the travelling salesman problem[J]. IEEE Trans Evol Comp,1997,1(1):53-66.
  • 2[2]Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents[J]. IEEE Trans SMC: Part B,1996,26(1):29-41.
  • 3[3]Gambardella L M, Dorigo M. Solving symmetric and asymmetric TSPs by ant colonies[A]. Proc IEEE Int Conf Evol Comp[C]. Piscataway, 1996.622-627.
  • 4[4]Boryczka U, Boryczka M. Generative policies in ant systems for scheduling[A]. 6th European Congr Intell Tech Soft Comp[C]. Bruxelles,1998.1:382-386.
  • 5[5]Boryczka U. Learning with delayed rewards in ant sys-tems for the job-shop scheduling problem[A]. First Int Conf Rough Sets Current Trends Comp[C]. Bruxelles,1998.271-274.
  • 6[6]Gambardella L M, Taillard E D, Dorigo M. Ant colonies for the quadratic assignment problem[J]. J Oper Res Soci,1999,50(2):167-176.
  • 7[7]Maniezzo V,Dorigo M,Colorni A.Algodesk:An experimental comparison of eight evolutionary heuristics applied to the quadratic assignment problem[J]. European J Oper Res,1995,81(1):188-204.
  • 8[8]Maniezzo V. Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem[J]. Infor J Comp,1999,11(4):358-369.
  • 9[9]Maniezzo V, Colorni A. Ant system applied to the quadratic assignment problem[J]. IEEE Trans Knowl Data Eng,1999,11(5):769-778.
  • 10[10]Leguizamon G, Michalewicz Z. A new version of ant system for subset problems[A]. Proc Congr Evol Comp[C]. Darmstadt,1999.2:1459-1464.

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