General Tuning of Weights in MOEA/D

0469509 - ÚI 2017 RIV US eng C - Conference Paper (international conference)
Pilát, M. - Neruda, Roman
General Tuning of Weights in MOEA/D.
CEC 2016. IEEE Congress on Evolutionary Computation. New York: IEEE, 2016, s. 965-972. ISBN 978-1-5090-0623-6.
[CEC 2016. IEEE Congress on Evolutionary Computation. Vancouver (CA), 24.07.2016-29.07.2016]
R&D Projects: GA ČR GA15-19877S
Institutional support: RVO:67985807
Keywords : computational intelligence * evolutionary algorithms * optimization
Subject RIV: IN - Informatics, Computer Science

In decomposition based algorithms the quality of the resulting solutions depends on the weights used in the decomposition scheme. Usually the weights are generated in the beginning and remain fixed during the evolution, which may lead to poor distribution of solutions along the Pareto front. In this paper, we describe an extension of the popular MOEA/D algorithm which is able to tune the weights in order to find a set of solutions which maximizes a user specified objective. This adaptation is added as a new step to the algorithm which uses an approximation of the Pareto front to find suitable points in the objective space. These points are translated back into weights in such way to lead MOEA/D to find these points.
Permanent Link: http://hdl.handle.net/11104/0267291