An estimation of distribution algorithm based on decomposition for the multiobjective TSP

Abstract

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has gained much attention recently. It is suitable to use scalar objective optimization techniques for dealing with multiobjective optimization problems. In this paper, we propose a new approach, named multiobjective estimation of distribution algorithm based on decomposition (MEDA/D), which combines MOEA/D with probabilistic model based methods for multiobjective traveling salesman problems (MOTSPs). In MEDA/D, an MOTSP is decomposed into a set of scalar objective sub-problems and a probabilistic model, using both priori and learned information, is built to guide the search for each subproblem. By the cooperation of neighbor sub-problems, MEDA/D could optimize all the sub-problems simultaneously and thus find an approximation to the original MOTSP in a single run. The experimental results show that MEDA/D outperforms BicriterionAnt, an ant colony based method, on a set of test instances and MEDA/D is insensible to its control parameters.