A Multiobjective Estimation of Distribution Algorithm Based on Artificial Bee Colony

Abstract

In this paper, we propose a hybrid Multiobjective Estimation of Distribution Algorithm based on Artificial Bee Colonies and Clusters (MOEDABC) to solve multiobjective optimization problems with continuous variables. This algorithm is inspired in the organization and division of work in a bee colony and employs techniques from estimation of distribution algorithms. To improve some estimations we also employ clustering methods in the objective space. In the MOEDABC model, the colony consists of four groups of bees, each of which with its specific role in the colony: employer bees, onlookers, farmers and scouts. Each role is associated to specific tasks in the optimization process and employs different estimation of distribution methods. By combining estimation of distribution, clusterization of the objective domain, and the crowding distance assignment of NSGA-II, it was possible to extract more information about the optimization problem, thus enabling an efficient solution of large scale decision variable problems. Regarding the test problems, quality indicators, and GDE3, MOEA/D and NSGA-II methods, the combination of strategies incorporated into the MOEDABC algorithm has presented competitive results, which indicate this method as a useful optimization tool for the class of problems considered.