An efficient mixture sampling model for gaussian estimation of distribution algorithm

Estimation of distribution algorithm (EDA) is a stochastic optimization algorithm based on probability distribution model and has been widely applied in global optimization. However, the random sampling of Gaussian EDA (GEDA) usually suffers from the poor diversity and the premature convergence, which severely limits its performance. This paper analyzes the shortcomings of the random sampling and develops an efficient mixture sampling model (EMSM). EMSM can explore more promising regions and utilize the unsuccessful mutation vectors, which achieves a good tradeoff between the diversity and the convergence. Moreover, the feasibility analysis of EMSM is studied. A new GEDA variant named EMSM-EDA is developed, which combines EMSM with enhancing Gaussian estimation of distribution algorithm (EDA(2)). The experimental results on IEEE CEC2013 and IEEE CEC2014 test suites demonstrate that EMSM-EDA is efficient and competitive. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

Estimation of distribution algorithm (EDA) is a stochastic optimization algorithm based on probability distribution model. This paper proposes an efficient mixture sampling model (EMSM) to address the poor diversity and premature convergence issues in Gaussian EDA (GEDA). By combining EMSM with enhancing Gaussian estimation of distribution algorithm (EDA(2)), a new GEDA variant named EMSM-EDA is developed. Experimental results demonstrate that EMSM-EDA is efficient and competitive.