Computer Science ›› 2017, Vol. 44 ›› Issue (1): 264-270.doi: 10.11896/j.issn.1002-137X.2017.01.049

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Research of Many-objective Evolutionary Algorithm Based on Alpha Dominance

LIN Meng-man, ZHOU Huan and WANG Li-ping   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The classic multiobjective evolutionary algorithms based on the Pareto dominance solve the problems with 2 to 3 objectives effectively.However,when dealing with many-objective problems,as the number of dominance resistant solutions is rapidly increasing owing to the increase of the objectives,the existed multiobjective algorithms lack of the selection pressure towards the Pareto front,and the optimization effect becomes bad.In this paper,we analyzed the influence of different alpha values,then provided strict Pareto layer,and selected the relatively sparse solution as candidate solutions in the same layer.At last,we proposed a new many-objective evolutionary algorithm based on alpha partial order and congestion distance sampling.The performance of the algorithms was evaluated by generation distances(GD),spacing(SP),highpervolume(HV) on the DTLZ problems.The experimental results show that the convergence of the algorithm improves greatly through eliminating the DRSs.Compared with the NSGA-II,MOEA/D and MOEA/D-DU,the overall quality of the solutions by the improved algorithms increases greatly.

Key words: Many-objective optimization,Dominance resistance solutions,Congestion distance,Highpervolume

[1] CHENG R,JIN Y,OLHOFER M,et al.A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization[J].IEEE Transactions on Evolutionary Computation,2016 .
[2] HOU Wei,DONG Hong-bin,YIN Gui-sheng.Enhanced Multi-objective Evolutionary Algorithm Based on Decomposition[J].Computer Science,2014,1(2):114-118.(in Chinese) 侯薇,董红斌,印桂生.一种改进的基于分解的多目标进化算法[J].计算机科学,2014,41(2):114-118.
[3] ISHIBUCHI H,TSUKAMOTO N,HITOTSUYANAGI Y,etal.Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems[C]∥Congerence on Genetic and Evolutionary Computation.2008:649-656.
[4] KOPPEN M,YOSHIDA K.Substitute distance assignments inNSGA-II for handling Many-objective optimization problems[C]∥Evolutionary Multi-Criterion Optimization.2007:727-741.
[5] CORNE D,KNOWLES J.Techniques for highly multiobjective optimization:Some nondominated points are better than others[C]∥Conference on Genetic and Evolutionary Computation.2007:773-780.
[6] KUKKONEN S,LAMPINEN J.Ranking-dominance and many-objective optimization[C]∥IEEE Congress on Evolutionary Computation.2007:3983-3990.
[7] SATO H,AGUIRRE H,TANAKA K.Controlling dominance area of solutions and its impact on the performance of MOEAs[C]∥Evolutionary Multi-Criterion Optimization.2007:5-20.
[8] ZITZLER E,KNZLI S.Indicator-based selection in multiob-jective search[C]∥Parallel Problem Solving from Nature-PPSN VIII.Springer Berlin Heidelberg,2004:832-842.
[9] BADER J,ZITZLER E.HypE:An algorithm for fast hypervo-lume-based many-objective optimization[J].Evolutionary computation,2011,19(1):45-76.
[10] ZHANG Q,LI H.MOEA/D:A multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,11(6):712-731.
[11] DEB K,JAIN H.An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach,part I:Solving problems with box constraints[J].IEEE Transactions on Evolutionary Computation,2014,18(4):577-601.
[12] IKEDA K,KITA H,KOBAYASHI S.Failure of Pareto-based MOEAs:does non-dominated really mean near to optimal?[C]∥Proceedings of the 2001 Congress on Evolutionary Computation,2001.IEEE,2001,2:957-962.
[13] 谭艳艳.几种改进的分解类多目标进化算法及其应用[D].西安:西安电子科技大学,2013.
[14] ZITZLER E,THIELE L,LAUMANNS M,et al.Performanceassessment of multiobjective optimizers:an analysis and review[J].IEEE Transactions on Evolutionary Computation,2003, 7(2):117-132.
[15] WANG Z,ZHANG Q,ZHOU A,et al.Adaptive ReplacementStrategies for MOEA/D[J].IEEE Transactions on Cybernetics,2015(1):1-13.
[16] GONG Dun-wei,LIU Yi-ping,SUN Xiao-yan,et al.ParallelMany-objective Evolutionary Optimization Using Objectives Decomposition[J].Acta Automatica Sinica,2015,1(8):1438-1451.(in Chinese) 巩敦卫,刘益萍,孙晓燕,等.基于目标分解的高维多目标并行进化优化方法[J].自动化学报,2015,41(8):1438-1451.
[17] ZHANG Yi,WAN Xing-yu,ZHENG Xiao-dong,et al.Cellular Genetic Algorithm for Multiobjective Optimization Based on Orthogonal Design[J].Acta Electronica Sinica,2016,4(1):87-94.(in Chinese) 张屹,万兴余,郑小东,等.基于正交设计的元胞多目标遗传算法[J].电子学报,2016,44(1):87-94.

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