一种基于偏好的多目标调和遗传算法
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Supported by me National Grand Fundamental Research 973 Program of China under GrantNo.2003CB314804(国家重点基础研究发展规划(973));the National Natural Science Foundation of China under Grant Nos.90104002,60303027(国家自然科学基金);Opening Foundation of State Key Laboratory for Novel Software Technologyin Nanjing University(南京大学计算机软件新技术国家重点实验室开放基金);the Natural Science Foundation of Anhui Province of China(安徽省自然科学基金)


A Preference-Based Multi-Objective Concordance Genetic Algorithm
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    摘要:

    最近涌现了各种进化方法来解决多目标优化问题,多数方法使用Pareto优胜关系作为选择策略而没有采用偏好信息.这些算法不能有效处理目标数目许多时的优化问题.通过在不同准则之间引入偏好来解决该问题,提出一种多目标调和遗传算法MOCGA(multi-objective concordance genetic algorithm).当同时待优化的目标数目增加时,根据决策者提供的信息使用弱优胜关系进行个体优劣的比较.这种算法被证明为能收敛至全局最优.对于目标数目为很多的优化问题,测试实验结果表明了这种新算法的有效性.

    Abstract:

    Recently various evolutionary approaches have been developed for multi-objective optimization. Most of them take Pareto dominance as their selection strategy and do not require any preference information. However these algorithms cannot perform well on problems involving many objectives. By introducing preferences among different criteria, a multi-objective concordance genetic algorithm (MOCGA) is proposed to deal with the problems in the paper. As the number of objectives to be simultaneously optimized increases, the weak dominance is used to compare among the individuals with decision-maker's information. It is proven that the algorithm can guarantee the convergence towards the global optimum. Experimental results of the multi-objective optimization benchmark problems demonstrate the validity of the new algorithm.

    参考文献
    [1]Van Veldhuizen DA, Lamont GB. Multi-Objective evolutionary algorithms: Analyzing the State-of-the-Art. IEEE Trans. on Evolutionary Computation, 2000,8(2): 125-147.
    [2]Coello CAC. List of Reference on Evolutionary Multi-objective Optimization. http://www.lania.mx/~ccoello/EMOO/EMOObib.html.
    [3]Fonseca CM, Fleming PJ. An overview of evolutionary algorithms in multi-objective optimization. Evolutionary Computation,1995,3(1):11-16.
    [4]Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans. on Evolutionary Computation, 1997,1(1):67-82.
    [5]Fonseca CM, Fleming PJ. Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. In:Stephanie Forrest, ed. Proc. of the 5th Int'l Conf. on Genetic Algorithms. University of Illinois at Urbana-Champaign: Morgan Kauffman Publishers, 1993.416-423.
    [6]Zitzler E, Thiele L. Multi-Objective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. on Evolutionary Computation, 1999,3(4):257-271.
    [7]Deb K, Agarwal S, Pratap A, Heyarivon T. A fast and elitist multi-objective genetic algorithm: NSGA-Ⅱ. IEEE Trans. Evolutionary Computation, 2002,6(2):182-197.
    [8]Dragan C, Ian CP. Designer's preferences and multi-objective preliminary design processes, In: Ian CP, ed. Proc. of the 4th Int'l Conf. on Adaptive Computing in Design and Manufacture. London: Springer, 2000. 249-260.
    [9]Dragan C, Ian CP. Preferences and their application in evolutionary multi-objective optimization. IEEE Trans. on Evolutionary Computation, 2002,6(1):42-57.
    [10]Coello CAC. Handling preferences in evolutionary multi-objective optimization: A survey. In: Coello CAC, ed. Proc. Congress of Evolutionary Computation. New Jersey: IEEE Service Center, 2000. 30-37.
    [11]Triantaphyllou E, Shu B, Nieto SS. Multi-Criteria decision making: An operations research approach. In: Webster JG, ed.Encyclopedia of Electrical and Electronics Engineering. New York: John Wiley & Sons, 1998. 175-186.
    [12]Deb K, Thiele L, Laumanns M, Laumanns M, Zitzler E. Scalable multi-objective optimization test problems. In: Yao X, ed. Proc.of the 2002 Congress on Evolutionary Computation. New Jersey: IEEE Service Center, 2002. 825-830.
    [13]Van Veldhuizen DA, Lamont GB. Evolutionary computation and convergence to a pareto front. In: Koza JR, ed. Late Breaking Papers at the Genetic Programming 1998 Conf. California: Stanford University Bookstore, 1998. 221-228.
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崔逊学,林闯.一种基于偏好的多目标调和遗传算法.软件学报,2005,16(5):761-770

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  • 收稿日期:2003-11-30
  • 最后修改日期:2004-05-11
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