摘要: 利用基于分解的多目标进化算法框架(MOEA/D),将混合策略的进化算法用于求解分解后的若干单目标优化子问题,提出了一种带局部搜索的基于分解的多目标混合策略进化算法(LMS-MOEA/D)。算法利用均匀设计产生子问题的聚合权重向量,混合交叉策略能够充分利用不同交叉算子的优势;同时算法针对演化过程收敛的特点,结合局部搜索策略,获得逼近Pareto前沿的最优解集。最后通过实验验证算法在多样性和收敛性方面的有效性。
[1] Deb K,Agrawal S,Pratap A,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II [J].IEEE Transactions on Evo-lutionary Computation,2002,6(2):182-197 [2] Zhou Ai-min,Qu B Y,Li Hui,et al.Multiobjective evolutionary algorithms:A survey of the state of the art [J].Swarm and Evolutionary Computation,2011,1(1):32-49 [3] Zhang Q F,Li H.MOEA/D:A multiobjective evolutionary algorithm based on decomposition [J].IEEE Transactions on Evolutionary Computation,2007,1(6):712-731 [4] 公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究 [J].软件学报,2009,0(2):271-289 [5] Li H,Zhang Q F.Multiobjective optimization problems withcomplicated pareto sets,MOEA/D and NSGA-II [J].IEEE Transactions on Evolutionary Computation,2009,3(2):284-302 [6] Tan Yan-yan,Jiao Yong-chang,Li Hong,et al.A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets [J].Information Sciences,2012,3(5):14-38 [7] Sindhya K,Miettinen K,Deb K.A hybrid framework for evolutionary multi-objective optimization [J].IEEE Transactions on Evolutionary Computation,2013,7(4):495-511 [8] Fang K T,Lin D K J,Winker P,et al.Uniform design:theory and application [J].Technometrics,2000,42(3):237-248 [9] He J,Yao X.A game-theoretic approach for designing mixedmutation strategies [C]∥Proceedings of the International Conference on Natural Computation.Berlin:Springer,2005:279-288 [10] Dong Hong-bin,He Jun,Huang Hou-kuan,et al.Evolutionary programming using a mixed mutation strategy [J].Information Sciences,2007,177(1):312-327 [11] Gong Mao-guo,Liu Chao,Jiao Li-cheng,et al.Hybrid immune algorithm with Lamarckian local search for multi-objective optimization [J].Memetic Computing,2010,2(1):47-67 [12] Hansen M P.Use of substitute scalarizing functions to guide alocal search based heuristic:The case of moTSP [J].Journal of Heuristics,2000,6(3):419-431 [13] Jaszkiewicz A.Genetic local search for multi-objective combinatorial optimization [J].European Journal of Operational Research,2002,137(1):50-71 [14] Sindhya K,Deb K,Miettinen K.A local search based evolutio-nary multi-objective approach for fast and accurate convergence [C]∥Proceedings of the Parallel Problem Solving from Nature-PPSN X.Berlin:Springer,2008:815-824 [15] Talbi E G,Rahoual M,Mabed M,et al.A hybrid evolutionaryapproach for multicriteria optimization problems:Application to the flow shop [C]∥Proceedings of the Evolutionary Multi-Criterion Optimization.Berlin:Springer,2001:416-428 [16] Sindhya K,Deb K,Miettinen K.Improving convergence of evolutionary multi-objective optimization with local search:A concurrent-hybrid algorithm [J].Natural Computing,2011,10(4):1407-1430 [17] Miettinen K.Nonlinear Multiobjective Optimization [M].Boston:Kluwer,1999 [18] Huband S,Hingston P,Barone L,et al.A review of multiobjective test problems and a scalable test problem toolkit [J].IEEE Transactions on Evolutionary Computation,2006,0(5):477-506 [19] Okabe T,Jin Y C,Olhofer M,et al.On test functions for evolutionary multi-objective optimization [C]∥Proceedings of Parallel Problem Solving From Nature-PPSN VIII.Berlin:Springer,2004:792-802 [20] Deb K,Sinha A,Kukkonen S.Multi-objective test problems,linkages,and evolutionary methodologies [C]∥Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (GECCO’06).New York:ACM,2006:1141-1148 [21] Li H,Zhang Q F.A multi-objective differential evolution based on decomposition for multi-objective optimization with variable linkages [C]∥Proceedings of Parallel Problem Solving from Nature—PPSN IX.Berlin:Springer,2006:583-592 |
No related articles found! |
|