Based on the study and analysis of A Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm (RM-MEDA), we propose An Improved Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm (IRM-MEDA). The IRM-MEDA had some features. 1) generate initial population with orthogonal design so that the individuals make a more representative distribution of the feasible solutions. 2)introduce a new convergence criterion to determine when the genetics-based method, i.e. crossover, mutation and when the EDA-based method should be used to generate offspring.3)combine genetics-based and model-based offspring generation instead of only model-based method in RM-MEDA. The experiment result on a number of test problems proved that An Improved Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm is able to find much better convergence near the the true Pareto-optimal solutions and better spread of solutions than RM-MEDA.
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