期刊文献+

多目标0/1背包问题MOEA求解中的修复策略 被引量:2

Repair Strategies for Multiobjective 0/1 Knapsack Problem in MOEA
原文传递
导出
摘要 多目标进化算法在求解多目标0/1背包问题时常使用修复策略来满足容量约束.文中更全面地考虑物品对各个背包的不同影响,提出两种加权修复策略,分别基于背包容量和容量约束违反程度,并应用于经典算法SPEA2中.在9个标准MOKP测试实例上的实验结果表明,采用该修复策略的SPEA2算法能更有效地收敛到Pareto最优前沿. A repair strategy is often adopted to guarantee feasibility of the muhiobjective evolutionary algorithms for muhiobjective 0/1 knapsack problem (MOKP). In this paper, impacts of each item on all knapsacks are much considered and two novel repair strategies are proposed based on the knapsack capacities and constraint violations, respectively. The two novel strategies are applied to SPEA2 to solve MOKP. The experimental results on 9 standard test cases of MOKP demonstrate that SPEA2 with the proposed repair strategies has better convergence to the Pareto-optimal front.
出处 《模式识别与人工智能》 EI CSCD 北大核心 2009年第4期519-526,共8页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金委海外青年学者合作研究基金资助项目(No.60428202)
关键词 多目标进化算法(MOEA) 多目标0/1背包问题(MOKP) 进化多目标优化 加权修复策略 Multiobjective Optimization Evolutionary Algorithm (MOEA), Muhiobjective 0/1Knapsack Problem (MOKP), Evolutionary Muhiobjective Optimization, WeightedScalar Repair Strategy
  • 相关文献

参考文献15

  • 1Deb K. Multi-Objective Optimization Using Evolutionary Algorithms. Chicester, UK: John Wiley & Sons, 2001.
  • 2谢涛,陈火旺,康立山.多目标优化的演化算法[J].计算机学报,2003,26(8):997-1003. 被引量:126
  • 3赵曙光,焦李成,王宇平,杨万海.基于均匀设计的多目标自适应遗传算法及应用[J].电子学报,2004,32(10):1723-1725. 被引量:10
  • 4Coello C C A. Evolutionary Multi-Objective Optimization: A Historical View of the Field. IEEE Computational Intelligence Magazine, 2006, 1(1): 28-36.
  • 5Zitzler E, Laumanns M, Thiele L. SPEA2 : Improving the Strength Pareto Evolutionary Algorithm. TIK-Report, 103, Zurich, Switzerland : Swiss Federal Institute of Technology. Computer Engineering and Networks Laboratory (TIK) , 2001.
  • 6Deb K, Pratap A, Agarwal S, et al. A Fast and Elitist Muhiobjective Genetic Algorithm: NSGA-Ⅱ. IEEE Trans on Evolutionary Computation, 2002, 6(2) : 182 - 197.
  • 7Knowles J D, Come D W. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 2000, 8(2): 149-172.
  • 8Zitzler E, Thiele L. Muhiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans on Evolutionary Computation, 1999, 3(4) : 257 -271.
  • 9Chu P C, Beasley J E. A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics, 1998,4( 1 ) : 63 -86.
  • 10Knowles J D, Come D W. M-PAES: A Memetic Algorithm for Muhiobjective Optimization//Proc of the Congress on Evolutionary Computation. La Jolla, USA, 2000,Ⅰ: 325- 332.

二级参考文献34

  • 1Charnes A, Cooper W W. Management Models and Industrial Applications of Linear Programming, Volume 1. New York:John Wiley, 1961.
  • 2Ijiri Y. Management Goals and Accounting for Control. Amsterdan: North Holland, 1965.
  • 3Hajela P, Lin C Y. Genetic search strategies in multicriterion optimal design. Structural Optimization, 1992, 4 : 99 - 107.
  • 4Chen Y L, Liu C C. Multiobjective VAR planning using the goal-attainment method, IEE Proceedings on Generation,Transmission and Distribution, 1994,141 (3) :227 -232.
  • 5Coello C A C, Christiansen A D, Aguirre A H. Using a new GA- based multiobjective optimization technique for the design of robot arms. Robotica, 1998,16:401-414.
  • 6Fujita K, Hirokawa N, Akagi S, Kitamura S, Yokohata H.Multi-objective optimal design of automotive engine using genetic algorithm. In: Proceedings of DETC'98-ASME Design Engineering Technical Conferences, 1998.
  • 7Cvetkovic D, Parmee I C. Genetic algorithm-based multi-objective optimization and conceptual engineering design, Washington DC, 1999. 29-36.
  • 8Zitzler E, Thiele L. Multiobjective optimization using evolutionary algorithms-a comparative case study. In: Eiben A E.Back T, Schoenauer M, Schwefel H P eds. Parallel Problem Solving from Nature, Berlin, Germany: Springer, 1998. 292-301.
  • 9Knowles J, Corne D. The Pareto archived evolution strategy:A new baseline algorithm for multiobjective optimization. In:Proceedings of the 1999 Congress on Evolutionary Computation, Washington DC, 1999. 98-105.
  • 10Coello C A C, Christiansen A D. Two new GA- based methods for multiobjective optimization. Civil Engineering Systems,1998, 15(3) :207-243.

共引文献133

同被引文献20

引证文献2

二级引证文献19

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部