A multiagent architecture for solving combinatorial optimization problems through metaheuristics

This article introduces MAM - multiagent architecture for metaheuristics, whose objective is to combine metaheuristics, through the multiagent approach, for solving combinatorial optimization problems. In this architecture, each metaheuristic is developed in the form of an autonomous agent, cooperatively interacting in an environment. This interaction between one or more agents is carried out through information exchange in the search space of the problem, seeking to improve the same objective. MAM is a flexible architecture, which can be used for solving different optimization problems, without the need to rewrite algorithms. In this paper, the MAM architecture is specialized for genetic algorithm (GA), iterated local search (ILS) and variable neighborhood search (VNS) metaheuristics in order to solve the vehicle routing problem with time windows (VRPTW). Computational tests were performed and results are presented, showing the effectiveness of the proposed architecture.