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融合张角拥挤控制策略的高维多目标优化

陈振兴 严宣辉 吴坤安 白猛

陈振兴, 严宣辉, 吴坤安, 白猛. 融合张角拥挤控制策略的高维多目标优化. 自动化学报, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
引用本文: 陈振兴, 严宣辉, 吴坤安, 白猛. 融合张角拥挤控制策略的高维多目标优化. 自动化学报, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
CHEN Zhen-Xing, YAN Xuan-Hui, WU Kun-An, BAI Meng. Many-objective Optimization Integrating Open Angle Based Congestion Control Strategy. ACTA AUTOMATICA SINICA, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
Citation: CHEN Zhen-Xing, YAN Xuan-Hui, WU Kun-An, BAI Meng. Many-objective Optimization Integrating Open Angle Based Congestion Control Strategy. ACTA AUTOMATICA SINICA, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555

融合张角拥挤控制策略的高维多目标优化


DOI: 10.16383/j.aas.2015.c140555
详细信息
    作者简介:

    陈振兴 福建师范大学数学与计算机科学学院硕士研究生. 主要研究方向为计算智能与数据挖掘.E-mail: czx_ky@yeah.net

    通讯作者: 严宣辉 福建师范大学数学与计算机科学学院副教授. 主要研究方向为人工智能与数据挖掘. E-mail: yan@fjnu.edu.cn
  • 基金项目:

    国家自然科学基金(61175123)资助

Many-objective Optimization Integrating Open Angle Based Congestion Control Strategy

More Information
  • Fund Project:

    Supported by National Natural Science Foundation of China (61175123)

  • 摘要: 对于高维多目标优化问题,随着目标维数的增加,种群中非被支配解的比例剧增, 严重降低了种群的进化压力.为了对数量众多的非被支配解进行有效的拥挤控制并提升种群的多样性, 本文在提出张角概念的基础上设计了一种新的拥挤控制策略(Congestion control strategy based on open angle, CCSOA),它的时间复杂度并不会随着目标维数的增加而增大. 与目前优秀的进化多目标优化(Evolutionary multiobjective optimization, EMO)算法IBEA (Indicator-based evolutionary algorithm)、NSGAIII (Nondominated sorting genetic algorithm III)和GrEA (Grid-based evolutionary algorithm)的比较结果表明, 融合了CCSOA的高维多目标优化算法在收敛效果和解集分布的均匀性两个方面均有较大的优势.
  • [1] Coello C C, Lamont G B, Van Veldhuizen D A. Evolutionary Algorithms for Solving Multi-objective Problems (2nd edition). New York: Springer, 2007. 3-57
    [2] [2] Gong M Guo, Jiao L C, Yang D D, Ma W P. Research on evolutionary multi-objective optimization algorithms. Journal of Software, 2009, 20(2): 271-289
    [3] Jiao Li-Cheng, Shang Rong-Hua, Ma Wen-Ping, Gong Mao-Guo, Li Yang-Yang, Liu Fang. Multi-Objective Optimization Immune Algorithm, the Theory and Application. Beijing: Science Press, 2010. 3-27(焦李成, 尚荣华, 马文萍, 公茂果, 李阳阳, 刘芳. 多目标优化免疫算法、理论和应用. 北京: 科学出版社, 2010. 3-27
    [4] [4] Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601
    [5] [5] Yang S X, Li M Q, Liu X H, Zheng J H. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 2013, 17(5): 721-736
    [6] Cui Xun-Xue. Multiobjective Evolutionary Algorithms and Their Applications. Beijing: National Defence Industry Press, 2006. (崔逊学. 多目标进化算法及其应用. 北京: 国防工业出版社, 2006.
    [7] Zheng Jin-Hua. Multiobjective Evolutionary Algorithms and Their Application. Beijing: Science Press, 2007. (郑金华. 多目标进化算法及其应用. 北京: 科学出版社, 2007.
    [8] [8] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197
    [9] [9] Corne D W, Jerram N R, Knowles J D, Oates M J. PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 2001 Genetic and Evolutionary Computation Conference. San Francisco, USA: Morgan Kaufmann Publishers, 2001. 283-290
    [10] Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorithm. In: Proceedings of the 2002 International Conference on Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems. Berlin: Springer, 2002. 95-100
    [11] Zhang Q F, Li H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731
    [12] Bi Xiao-Jun, Zhang Yong-Jian, Shen Ji-Hong. High-dimensional multi-objective multi-directional co-evolutionary algorithm. Control and Decision, 2014, 29(10): 1737-1743(毕晓君, 张永建, 沈继红. 高维多目标多方向协同进化算法. 控制与决策, 2014, 29(10): 1737-1743
    [13] Deb K, Mohan M, Mishra S. Evaluating the -domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evolutionary Computation, 2005, 13(4): 501-525
    [14] Sinha A, Saxena D K, Deb K, Tiwari A. Using objective reduction and interactive procedure to handle many-objective optimization problems. Applied Soft Computing, 2013, 13(1): 415-427
    [15] Jaimes A L, Coello C A C, Chakraborty D. Objective reduction using a feature selection technique. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. New York: ACM, 2008. 673-680
    [16] Zitzler E, Knzli S. Indicator-based selection in multiobjective search. In: Proceedings of the 8th International Conference. Parallel Problem Solving from Nature-PPSN VIII. Birmingham, UK: Springer, 2004. 832-842
    [17] Gong Dun-Wei, Ji Xin-Fang, Sun Xiao-Yan. Solving many-objective optimization problems using set-based evolutionary algorithms. Chinese Journal of Electronics, 2014, 42(1): 77-83(巩敦卫, 季新芳, 孙晓燕. 基于集合的高维多目标优化问题的进化算法. 电子学报, 2014, 42(1): 77-83
    [18] Jiao L C, Wang H D, Shang R H, Liu F. A co-evolutionary multi-objective optimization algorithm based on direction vectors. Information Sciences, 2013, 228: 90-112
    [19] Reynoso-Meza G, Sanchis J, Blasco X, Martnez M. Design of continuous controllers using a multiobjective differential evolution algorithm with spherical pruning. Applications of Evolutionary Computation. Berlin: Springer-Verlag, 2010. 532-541
    [20] Tang L X, Wang X P. A Hybrid multiobjective evolutionary algorithm for multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 2013, 17(1): 20-45
    [21] Zhan Wei. Research on Manifold Learning Algorithm for High-Dimensional Multi-Objective Optimization Problems [Ph.D. dissertation], China University of Geosciences, China, 2013.(詹炜. 求解高维多目标优化问题的流形学习算法研究 [博士学位论文], 武汉: 中国地质大学, 中国, 2013.)
    [22] Agrawal R B, Deb K, Agrawal R B. Simulated binary crossover for continuous search space. Complex Systems, 1995, 9: 115-148
    [23] Hinterding R. Gaussian mutation and self-adaption for numeric genetic algorithms. In: Proceedings of the 1995 IEEE Conference on Evolutionary Computation. Perth, WA, Australia: IEEE, 1995, 1: 384-389
    [24] Liu Y Z, Li S F. A new differential evolutionary algorithm with neighborhood search. Information Technology Journal, 2011, 10: 573-578
    [25] Deb K, Thiele L, Laumanns M, Zitzler E. Scalable Test Problems for Evolutionary Multiobjective Optimization. London: Springer, 2005. 105-145
    [26] Van Veldhuizen D A, Lamont G B. On measuring multiobjective evolutionary algorithm performance. In: Proceedings of the 2000 Congress on Evolutionary Computation. La Jolla, CA: IEEE, 2000, 1: 204-211
    [27] Zhou A M, Jin Y C, Zhang Q F, Sendhoff B, Tsang E. Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation. Vancouver, BC: IEEE, 2006. 892-899
    [28] Tan K C, Yang Y J, Goh C K. A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2006, 10(5): 527-549
    [29] Tamhane A C. Multiple comparisons in model I one-way ANOVA with unequal variances. Communications in Statistics, 1977, 6(1): 15-32
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出版历程
  • 收稿日期:  2014-07-30
  • 修回日期:  2015-02-02
  • 刊出日期:  2015-06-20

融合张角拥挤控制策略的高维多目标优化

doi: 10.16383/j.aas.2015.c140555
    基金项目:

    国家自然科学基金(61175123)资助

    作者简介:

    陈振兴 福建师范大学数学与计算机科学学院硕士研究生. 主要研究方向为计算智能与数据挖掘.E-mail: czx_ky@yeah.net

    通讯作者: 严宣辉 福建师范大学数学与计算机科学学院副教授. 主要研究方向为人工智能与数据挖掘. E-mail: yan@fjnu.edu.cn

摘要: 对于高维多目标优化问题,随着目标维数的增加,种群中非被支配解的比例剧增, 严重降低了种群的进化压力.为了对数量众多的非被支配解进行有效的拥挤控制并提升种群的多样性, 本文在提出张角概念的基础上设计了一种新的拥挤控制策略(Congestion control strategy based on open angle, CCSOA),它的时间复杂度并不会随着目标维数的增加而增大. 与目前优秀的进化多目标优化(Evolutionary multiobjective optimization, EMO)算法IBEA (Indicator-based evolutionary algorithm)、NSGAIII (Nondominated sorting genetic algorithm III)和GrEA (Grid-based evolutionary algorithm)的比较结果表明, 融合了CCSOA的高维多目标优化算法在收敛效果和解集分布的均匀性两个方面均有较大的优势.

English Abstract

陈振兴, 严宣辉, 吴坤安, 白猛. 融合张角拥挤控制策略的高维多目标优化. 自动化学报, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
引用本文: 陈振兴, 严宣辉, 吴坤安, 白猛. 融合张角拥挤控制策略的高维多目标优化. 自动化学报, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
CHEN Zhen-Xing, YAN Xuan-Hui, WU Kun-An, BAI Meng. Many-objective Optimization Integrating Open Angle Based Congestion Control Strategy. ACTA AUTOMATICA SINICA, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
Citation: CHEN Zhen-Xing, YAN Xuan-Hui, WU Kun-An, BAI Meng. Many-objective Optimization Integrating Open Angle Based Congestion Control Strategy. ACTA AUTOMATICA SINICA, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555
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