Abstract:
Many real world applications require optimizing multiple objectives simultaneously. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a new framewo...View moreMetadata
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.
Published in: 2012 IEEE Congress on Evolutionary Computation
Date of Conference: 10-15 June 2012
Date Added to IEEE Xplore: 02 August 2012
ISBN Information: