Many-Objective Evolutionary Algorithm Based on Dynamic Decomposition and Angle Penalty Distance
The optimization problems in multiple areas can be modelled as many-objective optimization problems,which can be solved using many-objective evolutionary algorithms.However,it is difficult to balance convergence and di-versity.To tackle this issue,this paper proposes a many-objective evolutionary algorithm based on dynamic decomposition and modified angle penalty distance referred to as DAEA(Duplication Analysis based Evolutionary Algorithm).DAEA de-composes the whole population into multiple clusters through dynamic decomposition,which is exempt from the predefined reference vectors and makes full use of the distribution information of the population itself to decompose.Then,DAEA se-lects solutions from each cluster based on modified angle penalty distance to balance convergence and diversity.Besides,DAEA operates mating selection according to Pareto dominance,knee points,and m-nearest angle binary tournament selec-tion.Compared with nine many-objective evolutionary algorithms on 27 many-objective optimization problems,DAEA is effective on many-objective optimization problems with various shapes of Pareto front and stable on different numbers of objectives.
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维普
