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, whi...

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Vydáno v:	Information sciences Ročník 608; s. 1157 - 1182
Hlavní autoři:	Dang, Qianlong, Gao, Weifeng, Gong, Maoguo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.08.2022
Témata:	Efficient mixture sampling model Evolutionary algorithm Gaussian estimation of distribution algorithm Premature convergence Efficient mixture sampling model Premature convergence Evolutionary algorithm Gaussian estimation of distribution algorithm
ISSN:	0020-0255, 1872-6291
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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 (EDA2). The experimental results on IEEE CEC2013 and IEEE CEC2014 test suites demonstrate that EMSM-EDA is efficient and competitive.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2022.07.016