基于局部线性嵌入与差分进化的MOEA/D算法
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摘要
针对基于分解的多目标进化算法选择压力低、收敛速度慢的问题,提出一种局部线性嵌入(LLE)差分进化算法。根据LLE特性降低种群目标空间维数,利用快速非支配排序对种群分支配解进行分层,进而通过差分进化操作提高种群收敛速度。实验结果表明,与dMOPSO算法相比,该算法在保证多样性的同时具有较高的选择压力和较快的收敛速度。
Aiming at the problem of low selection pressure and slow convergence speed for multi-objective evolutionary algorithm based on decomposition,a Local Linear Embedding(LLE) Differential Evolution(DE) algorithm is proposed.According to LLE feature,the spatial dimension of the population target is reduced,and the population bifurcation solution is layered by fast non-dominated sorting,and then the population convergence speed is improved by differential evolution operation.Experimental results show that compared with dMOPSO algorithm,the algorithm has a higher selection pressure and faster convergence speed while ensuring diversity.
Aiming at the problem of low selection pressure and slow convergence speed for multi-objective evolutionary algorithm based on decomposition,a Local Linear Embedding(LLE) Differential Evolution(DE) algorithm is proposed.According to LLE feature,the spatial dimension of the population target is reduced,and the population bifurcation solution is layered by fast non-dominated sorting,and then the population convergence speed is improved by differential evolution operation.Experimental results show that compared with dMOPSO algorithm,the algorithm has a higher selection pressure and faster convergence speed while ensuring diversity.
引文
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[2] ZHANG Q,LI H.MOEA/D:a multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,11(6):712-731.
[3] PRICE K,STORN R M,LAMPINEN J A.Differential evolution:a practical approach to global optimization (natural computing series)[M].Berlin,Germany:Springer,2005.
[4] LI H,ZHANG Q F.Multiobjective optimization problems with complicated Pareto sets,MOEA/D and NSGA-II[J].IEEE Transactions on Evolutionary Computation,2009,13(2):284-302.
[5] ZAPOTECAS-MARTíNEZ S,DERBEL B,LIEFOOGHE A,et al.Geometric differential evolution in MOEA/D:a preliminary study[C]//Proceedings of Mexican International Conference on Artificial Intelligence.Berlin,Germany:Springer,2015:364-376.
[6] PAVELSKI L M,DELGADO M R,ALMEIDA C P D,et al.ELMOEA/D-DE:extreme learning surrogate models in multi-objective optimization based on decomposition and differential evolution[C]//Proceedings of Brazilian Conference on Intelligent Systems.Washington D.C.,USA:IEEE Computer Society,2014:318-323.
[7] CHEN L Y,WANG B Z,LIU W Q,et al.Self-adaptive multi-objective differential evolutionary algorithm based on decomposition[C]//Proceedings of the 11th International Conference on Computer Science and Education.Washington D.C.,USA:IEEE Press,2016:610-616.
[8] WANG X P,TANG L X.An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization[J].Information Sciences,2016,348:124-141.
[9] SU Y X,CHI R.Multi-objective particle swarm-differential evolution algorithm [J].Neural Computing and Applications,2017,28(2):407-418.
[10] 郑金华,刘磊,李密青,等.差分选择策略在复杂多目标优化问题中的研究[J].计算机研究与发展,2015,52(9):2123-2134.
[11] 赵志伟,杨景明,呼子宇,等.基于角度邻域的多目标差分进化算法[J].控制理论与应用,2017(1):22-32.
[12] ROWEIS S T,SAUL L K.Nonlinear dimensionality reduction by locally linear embedding [J].Science,2000,290(5500):2323-2326.
[13] YUAN Y,XU H,WANG B,et al.A new dominance relation-based evolutionary algorithm for many-objective optimization[J].IEEE Transactions on Evolutionary Computation,2016,20(1):16-37.
[14] ISHIBUCHI H,MASUDA H,NOJIMA Y.A study on performance evaluation ability of a modified inverted generational distance indicator[C]//Proceedings of Conference on Genetic and Evolutionary Computation.New York,USA:ACM Press,2015:695-702.
[15] TSAI S J,SUN T Y,LIU C C,et al.An improved multi-objective particle swarm optimizer for multi-objective problems[J].Expert Systems with Applications,2010,37(8):5872-5886.