@inproceedings{63ce343d1c69413687bda36a8bd9c22e,
title = "A multiobjective evolutionary algorithm based on decomposition and probability model",
abstract = "Many real world applications require optimizing multiple objectives simultaneously. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a new framework for dealing with such kind of multiobjective optimization problems (MOPs). MOEA/D focuses on how to maintain a set of scalarized sub-problems to approximate the optimum of a MOP. This paper addresses the offspring reproduction operator in MOEA/D. It is arguable that, to design efficient offspring generators, the properties of both the algorithm to use and the problem to tackle should be considered. To illustrate this idea, a generator based on multivariate Gaussian models is proposed under the MOEA/D framework in this paper. In the new generator, both the local and global population distribution information is extracted by a set of Gaussian distribution models; new trial solutions are sampled from the probability models. The proposed approach is applied to a set of benchmark problems with complicated Pareto sets. The comparison study shows that the offspring generator is promising for dealing with continuous MOPs.",
keywords = "Multiobjective evolutionary algorithm, decomposition, probabilistic model",
author = "Aimin Zhou and Qingfu Zhang and Guixu Zhang",
year = "2012",
doi = "10.1109/CEC.2012.6252954",
language = "英语",
isbn = "9781467315098",
series = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",
booktitle = "2012 IEEE Congress on Evolutionary Computation, CEC 2012",
note = "2012 IEEE Congress on Evolutionary Computation, CEC 2012 ; Conference date: 10-06-2012 Through 15-06-2012",
}