BO-AMHM: Uma Arquitetura Multiagente para Hibridização de Meta-Heurísticas para problemas Biobjetivo

Abstract

Several researches have pointed the hybridization of metaheuristics as an e ective way to deal with combinatorial optimization problems. Hybridization allows the combination of di erent techniques, exploiting the strengths and compensating the weakness of each of them. MAHM is a promising adaptive framework for hybridization of metaheuristics, originally designed for single objective problems. This framework is based on the concepts of Multiagent Systems and Particle Swarm Optimization. In this study we propose an extension of MAHM to the bi-objective scenario. The proposed framework is called BOMAHM. To adapt MAHM to the bi-objective context, we rede ne some concepts such as particle position and velocity. In this study the proposed framework is applied to the biobjective Symmetric Travelling Salesman Problem. Four methods are hybridized: PAES, GRASP, NSGA-II and Anytime-PLS. Experiments with 11 bi-objective instances were performed and the results show that BO-MAHM is able to provide better non-dominated sets in comparison to the ones obtained by algorithms existing in literature as well as hybridized versions of those algorithms proposed in this work.