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

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