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多目标分层规划问题的最优均衡宽容值序列算法 被引量:4

Tolerance Payment Sequence Algorithm of Optimal Equilibrium for Multi-objective Stratified Programming Problems
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摘要 对多目标分层规划问题的宽容完全分层序列算法做改进,寻求各层次多目标子问题的最优均衡值和最优均衡解,针对上级优先层次对下级层次的宽容值,求出所有层次按优先级顺序的最优均衡解;给出多目标分层规划问题的最优均衡宽容完全分层序列算法,得到在一定宽容限下所有层次的帕雷托(Pareto)最优解。 The tolerance algorithm of stratified sequence for multi-objective stratified programming problems is improved. An optimal equilibrium value and some optimal equilibrium solutions are found out for every rank. The optimal equilibrium solutions of all stratified programming problems according to their priority - ranks are got, which is based on the tolerance payment of the superior to the subordinate. The tolerance payment sequence algorithm of optimal equilibrium for multi-objective stratified programming problems is developed, therefore, and Pareto optimum solutions to all the ranks within certain tolerance limits are obtained.
出处 《空军工程大学学报(自然科学版)》 CSCD 北大核心 2005年第1期83-86,共4页 Journal of Air Force Engineering University(Natural Science Edition)
基金 陕西省自然科学基金资助项目(2003A09)
关键词 多目标分层规划 宽容完全分层序列算法 最优均衡解 帕雷托最优解 multi- objective stratified programming tolerance algorithm of stratified sequence optimal equilibrium solution Pareto optimum solution
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  • 1胡毓达.群体决策的α-较多有效规则与多目标群体决策的α-比较数法[J].系统工程学报,1996,11(2):47-51. 被引量:17
  • 2胡毓达.实用名目标规划[M].上海科学技术出版社,1990..
  • 3[1]Boffey B.Distributed Computing:Associated Cobinatorial Problems[M].London:Blackwell Scientific Publications,1992.
  • 4[2]Bruck J,Goodman J W.A Generalized Convergence Theorem for Neural Networks and its Applications in Optimization[J].IEEE Transactions on Information Theory,1988,34(6):1089-1092.
  • 5[3]Looi C.Neural Network Methods in Combinatorial Optimization[J].Computers &Operations Resarch,1992,19(3):191-208.
  • 6[4]Matsuda S.Theoretical Characterizatins of Possibilities and Impossibilities of Hopfield Neural Networks in Solving Combinatorial Optimization Problems.[A].Proceedings of IEEE International Conference on Neural Networks [C].1994:4563-4566.
  • 7[5]Cichocki A,Unbehauen R.Neural Networks for Optimization and Signal Processing [M].Chichester:John Wiley &Sons,1993.
  • 8[6]Funabiki N,Nishikwa S.A Gradual Neural-Network Approach for Frequency Assignment in Satellite Communication Systems.[J].IEEE Transactions on Neural Networks,1997,8(6):1359-1370.
  • 9[7]Cimikowski R,Shope P.A Neural-Network Algorithm of a Graph Layout Problem.[J].IEEE transactions on neural networks,1996,7(2):341-345.
  • 10[8]Gong D,Gen M,Yamazaki G,et al.Neural Network Approach for General Asignment Problem[A]. Proceedings of 1995 IEEE International Conference on Neural Network(Vo1.4)[C].1995:1861-1866.

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  • 1周克民,李俊峰,李霞.结构拓扑优化研究方法综述[J].力学进展,2005,35(1):69-76. 被引量:208
  • 2王可东,陈偲.水下地形匹配等值线算法研究[J].宇航学报,2006,27(5):995-999. 被引量:17
  • 3Lin Y, Simaan M A. Non - inferior Nash Strategies for Multi - team Systems [ J]. Journal of Optimization Theory and Application, 2004, 120 (1): 29-51.
  • 4Salent S W, Shaffer G. Optimal Asymmetric Strategies in Research Joint Ventures [J]. International Journal of Industrial Organization, 1998, 16 (2): 195-208.
  • 5林锉云,董加礼.多目标最优化理论[M].长春:吉林教育出版社,1992.
  • 6Yu J, Xiang S W. On Essential Components of the Nash Equilibrium Points [ J ]. Nonlinear Analysis TMA, 1999, 38 (2) : 259 - 264.
  • 7Yang H, Yu J. On Essential Components of the Set of Weakly Pareto -Nash Equilibrium Points [ J]. Applied Mathematic Letter, 2002, 15 (3) : 553 - 560.
  • 8Bard J F. Convex Two Level Optimization [J]. Mathematical Programming, 1988, 40 ( 1 ) : 15 -27.
  • 9李登峰.微分对策及其应用[M].北京:国防工业出版社,2002..
  • 10袁亚湘 孙文瑜.最优化理论与方法[M].北京:科学出版社,2001..

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