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Overview of multi-objective optimization methods 被引量：2

Overview of multi-objective optimization methods
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摘要 To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper. To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.
作者 LeiXiujuan ShiZhongke
机构地区 DepartmentofAutomaticControl DepartmentofAutomaticControl
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2004年第2期142-146,共5页 系统工程与电子技术（英文版）
关键词 multi-objective optimization objective function Pareto optimality genetic algorithms simulated annealing fuzzy logical. multi-objective optimization, objective function, Pareto optimality, genetic algorithms, simulated annealing, fuzzy logical.
分类号 TP273 [自动化与计算机技术—检测技术与自动化装置]
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参考文献18

  • 1[1]Asim Karim. Multi-Objective Optimization Technique.http://www-aaa. eng. ohio-state. edu/～ salimul/pubs/multi_ obj. pdf, 2002.
  • 2[2]Johan Andersson. A Survey of Multiobjective Optimization in Engineering Design. Department of Mechanical Engineering Linkoping University, Technical Report: LiTHIKP-R4097, http://hydra. ikp. liu. se/～johan/files/ikp_ r_ 1097. pdf, 2001.
  • 3[3]Pekka Korhonen. Multiple Objective Programming Support. International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria, Interim Report IR-98-010/March, http://www. iiasa. ac. at/Publications/Documents/IR-98-010. pdf, 1998.
  • 4[4]Kishan Chetan Srigiriraju. Noninferior Surface Tracing Evolutionary Algorithm (NSTEA) for Multiobjective Optimization.A Thesis Submitted to the Graduate Faculty of North Carolina State University in Partial Fulfillment of the Requirements for the Degree of Masters of Science, http://www. lib. ncsu.edu/ etd/ public/ etd-13 311615 7100222 71/ etd. pdf, 1999.
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同被引文献12

  • 1Tao Ye,Peijun Shi,Jing’ai Wang,Lianyou Liu,Yida Fan,Junfeng Hu.China’s Drought Disaster Risk Management: Perspective of Severe Droughts in 2009–2010[J].International Journal of Disaster Risk Science,2012,3(2):84-97. 被引量：10
  • 2Tsutsui S, Ghosh A. Genetic algorithms with a robust solution searching scheme[J]. IEEE Trans. on Evolutionary Computation,1997,1(3):201 - 208.
  • 3Paenke I, Branke J, Yaochu J. Efficient search for robust solu tion by means of evolutionary algorithms and fitness approximation[J].IEEE Trans. on Evolutionary Computation, 2006,10 (4):405 - 420.
  • 4Jin Y C, Branke J. Evolutionary optimization in uncertain environments a survey[J]. IEEE Trans. on Evolutionary Computation, 2005, 9(3):303-317.
  • 5Deb K, Agrawal S, Pratap A, et al. A fast and elitist multi-objective genetic algorithm:NSGA Ⅱ[J].IEEE Trans. on Evolutionary Computation, 2002, 6(2) : 182 - 197.
  • 6Barrico C, Antunes C H. Robustness analysis in evolutionary multi objective optimization [M]. Antunes: Carlos Henggeler, 2006:7- 14.
  • 7Gunawan S, Azarm S. Multi-objective robust optimization using a sensitivity region concept[J]. Structural and Multidisciplinary Optimization, 2004, 29(1) :50- 60.
  • 8Li M, Azarm S, Aute V. A multi-objective genetic algorithm for robust design optimization[C]// Proceedings of the Conference on Genetic and Evolutionary Computation, Washington DC, USA. 2005:771 - 778.
  • 9Deb K, Gupta H. Introducing robustness in multi-objective optimization[J]. Evolutionary Computation, 2006, 14 (4): 463 -494.
  • 10Li Mingqiang, Kou Jisong, Lin Dan, et al. The foundation theory and application of the genetic algorithms [M]. Beijing: Science Press, 2002 : 26 - 44.

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Journal of Systems Engineering and Electronics
2004年 第2期

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