期刊文献+

基于Pareto最优概念的多目标进化算法研究 被引量:5

Research on Pareto optimal-based multiobjective evolutionary algorithms
下载PDF
导出
摘要 基于Pareto最优概念的多目标进化算法已成为多目标优化问题研究的主流方向。详细介绍了该领域的经典算法,重点阐述了各种算法在种群快速收敛并均匀分布于问题的非劣最优域上所采取的策略,并归纳了算法性能评估中需要进一步研究的几个问题。 The Pareto optimal-based multi-objective evolutionary algorithm which is used to deal with multi-objective optimization problems has become a hot research topic.In this paper,some state-of-the-art algorithms in this research field are described firstly.Then,strategies adopted by various kinds of algorithms about finding the non-dominated set of solutions and distribute them uniformly in the Pareto front are elaborated.Lastly,several research points of performance evaluation which need to be further study are summarized.
出处 《计算机工程与应用》 CSCD 北大核心 2008年第27期58-61,共4页 Computer Engineering and Applications
关键词 多目标进化算法 PARETO最优 非劣解排序 适应度共享 精英策略 性能评估 muhiobjeetive evolutionary algorithms Pareto optimal nondominated sorting fitness sharing elitism performance measure
  • 相关文献

参考文献23

  • 1Chankong V,Haimes Y Y.Multiobjective decision making theory and methodology[M].New York:North-Holland,1983. 被引量:1
  • 2Hans A E.Multicriteria optimization for highly accurate systems[M]. New York :Plenum Press, 1988. 被引量:1
  • 3Srinivas N,Deb K.Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms[J].Evolutionary Computation, 1995,2 ( 3 ) : 221-248. 被引量:1
  • 4Rosenberg R S.Simulation of genetic populations with biochemical properties[D].Michigan : University of Michigan, 1967. 被引量:1
  • 5Schaffer J D.Some experiments in machine learning using vector evaluated genetic algorithms{D].Tennessee:Vanderbih University Electrical Engineering, 1984. 被引量:1
  • 6Richardson J T,Palmer M R,Liepins G,et al.Some. Guidelines for genetic algorithms with penalty functions[C]//Proc 3rd Int Conf on Genetic Algorithms.[S.l.]:Morgan Kaufmann,1989. 被引量:1
  • 7Fonseca C M,Fleming P J.Genetic algorithm[C]//Proceedings of the Fifth International Conference,San Mateo,1993. 被引量:1
  • 8Horn J,Nafpliotis N,Goldberg D E.A niched pareto genetic algorithm for multiobjective optimization[C]//Proceedings of the First IEEE Conference on Evolutionary Computation. Piscataway,New Jersey:IEEE World Congress on Computational Intelligence,1994: 82-87. 被引量:1
  • 9Knowles J,Come D. The pareto arehived evolution strategy:a new baseline algorithm for muhiobjective optimization[C]//Proceedings of the 1999 Congress on Evolutionary Computation.Piscataway,New Jersey:IEEE Service Center, 1999:98-105. 被引量:1
  • 10Zitzler E,Thiele L.Muhiobjective evolutionary algorithms:a comparative case study and the strength Pareto approach [J].IEEE Transactions on Evolutionary Computation, 1999,3 (4) : 257-271. 被引量:1

同被引文献46

  • 1方勇,李渝曾.电力市场中激励性可中断负荷合同的建模与实施研究[J].电网技术,2004,28(17):41-46. 被引量:54
  • 2张连文,夏人伟.Pareto最优解及其优化算法[J].北京航空航天大学学报,1997,23(2):206-211. 被引量:13
  • 3刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007,22(7):721-729. 被引量:290
  • 4Willis H L. Analytical methods and rules of thumb for modeling DG - distributionaction [ C ]. IEEE Power Engineering Society Summer Meeting, USA, July,2000 ( 3 ) : 1643 - 1644. 被引量:1
  • 5Wen Zhang, Yutian Liu. Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm . Electrical Power and Energy System,2008,20:525 - 532. 被引量:1
  • 6黎静华,韦化.基于内点法的机组组合模型[J].电网技术,2007,31(24):28-34. 被引量:20
  • 7Tsikalakis A G, Hatziargyriou N D. Centralized control for optimizing microgrids operation[J]. IEEE Transactions on Energy Conversion, 2008, 23(1): 241-248. 被引量:1
  • 8Bagherian A, Tafreshi S M M. A developed energy management system for a microgrid in the competitive electricity market[C]//Proceedings of IEEE Bucharest Power Tech. Bucharest: IEEE, 2009: 1-6. 被引量:1
  • 9Mohamed F A, Koivo H N. Online management of microgrid with battery storage using multiobjective optimization[C]//Proceedings of International Conference on Power Engineering, Energy and Electrical Drives. Setubal, Portugal: IEEE; 2007: 231-236. 被引量:1
  • 10Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. 被引量:1

引证文献5

二级引证文献59

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