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局部抽象凸区域剖分差分进化算法

DOI: 10.16383/j.aas.2015.c140680, PP. 1315-1327

Keywords: 差分进化,区域剖分,全局优化,下界估计,抽象凸

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Abstract:

?在差分进化算法框架下,结合抽象凸理论,提出一种局部抽象凸区域剖分差分进化算法(Localpartitionbaseddifferentialevolution,LPDE).首先,通过对新个体的邻近个体构建分段线性下界支撑面,实现搜索区域的动态剖分;然后,利用区域剖分特性逐步缩小搜索空间,同时根据下界估计信息指导种群更新,并筛选出较差个体;其次,借助下界支撑面的广义下降方向作局部增强,并根据进化信息对搜索区域进行二次剖分;最后,根据个体的局部邻域下降方向对部分较差个体作增强处理.数值实验结果表明了所提算法的有效性.

References

[1]  Walsh G R. Methods of Optimization. London: Wiley Press, 1975.
[2]  Nelder J A, Mead R. A simplex method for function minimization. The Computer Journal, 1965, 7(4): 308-313
[3]  Adjiman C S, Dallwig S, Floudas C A, Neumaier A. A global optimization method, αBB, for general twice-differentiable constrained NLPs: I. Theoretical advances. Computers & Chemistry Engineering, 1998, 22(9): 1137-1158
[4]  Adjiman C S, Androulakis I P, Floudas C A. A global optimization method, αBB, for general twice-differentiable constrained NLPs: II. Implementation and computational results. Computers & Chemistry Engineering, 1998, 22(9): 1159-1179
[5]  Skj?l A, Westerlund T, Misener R, Floudas C A. A generalization of the classical αBB convex underestimation via diagonal and nondiagonal quadratic terms. Journal of Optimization Theory and Applications, 2012, 154(2): 462-490
[6]  Beliakov G. Cutting angle method ---a tool for constrained global optimization. Optimization Methods and Software, 2004, 19(2): 137-151
[7]  Bagirov A M, Rubinov A M. Cutting angle method and a local search. Journal of Global Optimization, 2003, 27(2-3): 193-213
[8]  Beliakov G. Geometry and combinatorics of the cutting angle method. Optimization, 2003, 52(4-5): 379-394
[9]  Floudas C A, Gounaris C E. A review of recent advances in global optimization. Journal of Global Optimization, 2009, 45(1): 3-38
[10]  Das S, Suganthan P N. Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 4-31
[11]  Wang Da-Zhi, Liu Shi-Xin, Guo Xi-Wang. A multi-agent evolutionary algorithm for solving total tardiness permutation flow-shop scheduling problem. Acta Automatica Sinica, 2014, 40(3): 548-555 (王大志, 刘士新, 郭希旺. 求解总拖期时间最小化流水车间调度问题的多智能体进化算法. 自动化学报, 2014, 40(3): 548-555
[12]  Storn R, Price K. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341-359
[13]  Islam S M, Das S, Ghosh S, Roy S, Suganthan P N. An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012, 42(2): 482-500
[14]  Hu Rong, Qian Bin. A hybrid differential evolution algorithm for stochastic flow shop scheduling with limited buffers. Acta Automatica Sinica, 2009, 35(12): 1580-1586 (胡蓉, 钱斌. 一种求解随机有限缓冲区流水线调度的混合差分进化算法. 自动化学报, 2009, 35(12): 1580-1586)
[15]  Stoean C, Preuss M, Stoean R, Dumitrescu D. Multimodal optimization by means of a topological species conservation algorithm. IEEE Transactions on Evolutionary Computation, 2010, 14(6): 842-864
[16]  Kaelo P, Ali M M. A numerical study of some modified differential evolution algorithm. European Journal of Operational Research, 2006, 169(3): 1176-1184
[17]  Cai Y Q, Wang J H. Differential evolution with neighborhood and direction information for numerical optimization. IEEE Transactions on Cybernetics, 2013, 43(6): 2202-2215
[18]  Bhattacharya A, Chattopadhyay P K. Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Transactions on Power Systems, 2010, 25(4): 1955-1964
[19]  Wang Y, Cai Z X, Zhang Q F. Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 55-66
[20]  Gong W Y, Cai Z H. Differential evolution with ranking-based mutation operators. IEEE Transactions on Cybernetics, 2013, 43(6): 2066-2081
[21]  Zhang Gui-Jun, He Yang-Jun, Guo Hai-Feng, Feng Yuan-Jing, Xu Jian-Ming. Differential evolution algorithm for multimodal optimization based on abstract convex underestimation. Journal of Software, 2013, 24(6): 1177-1195 (张贵军, 何洋军, 郭海锋, 冯远静, 徐建明. 基于广义凸下界估计的多模态差分进化算法. 软件学报, 2013, 24(6): 1177-1195)
[22]  Deng Yong-Yue, Zhang Gui-Jun. Multimodal optimization based on local abstract convexity support hyperplanes. Control Theory & Applications, 2014, 31(4): 458-466 (邓勇跃, 张贵军. 基于局部抽象凸支撑面的多模态优化算法. 控制理论与应用, 2014, 31(4): 458-466)
[23]  Rubinov A M. Abstract convexity and global optimization. Nonconvex Optimization and Its Applications. Dordrecht: Kluwer Academic Publishers, 2000.
[24]  Bagirov A M, Rubinov A M. Global minimization of increasing positively homogeneous functions over the unit simplex. Annals of Operations Research, 2000, 98(1-4): 171-187
[25]  Zhang Gui-Jun, Zhou Xiao-Gen. Population-based global optimization algorithm using abstract convex underestimate. Control and Decision, 2015, 30(6): 1116-1120(张贵军, 周晓根. 基于抽象凸下界估计的群体全局优化算法. 控制与决策, 2015, 30(6): 1116-1120)
[26]  Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 2009, 13(2): 398-417
[27]  Corder G W, Foreman D I. Nonparametric Statistics for Non-statisticians: a Step-by-step Approach. Hoboken, NJ: Wiley Press, 2009.
[28]  Chen Bao-Lin. Theory and Methods of Optimization (2nd Edition). Beijing: Tsinghua University Press, 2005. (陈宝林. 最优化理论与算法. 第2版. 北京: 清华大学出版社, 2005.)

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