Multiobjective Distribution Estimation Algorithm Based on Independent Component Analysis
NIE Kai MENG
Lingjing
LI Dong
Abstract：Multiobjective estimation of distribution algorithm based on independent component analysis(ICA-MOEDA) is proposed for solving multiobjective optimization problems .The non-Gaussian probabilistic graphical model is introduced in the nonlinear variable linkage continuous optimization problems .Then the ICA model is performed on the parent population to get the new independent population which is clustered .The selection procedure is based on the non-dominated sorting of NSGA-Ⅱ and crowding distance which choose the best offspring enter next generation .Compared with other two evolution-ary multiobjective algorithms ,simulation results show that the improvement algorithm has good convergence and diversity performance on ZDT2-2 ,ZDT4-2 ,ZDT6-2 and F5 benchmark instances and the variables are not required to subject to Gaussian distribution .
Keywords：multiobjective optimizationestimation of distribution algorithm (EDA )independent component analysis (ICA)NSGA-Ⅱ
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 976-979,1062 )