A multiagent, dynamic rank-driven multi-deme architecture for real-valued multiobjective optimization

摘要

Multiobjective real parameter optimization is a challenging problem in majority of engineering applications. This paper presents a creative multiagent and dynamic multi-deme architecture based on a novel collaboration mechanism for the solution of multiobjective real-parameter optimization problems. The proposed architecture comprises a number of multiobjective metaheuristic agents that act on subsets of a population based on a cyclic assignment order. The proposed multiagent architecture works iteratively in sessions including two consecutive phases: in the first phase, a population of solutions is divided into subpopulations based on dominance ranks of its elements. In the second phase, each multiobjective metaheuristic is assigned to work on a subpopulation based on a cyclic or round-robin order. Hence, each metaheuristic operates on a different-rank subpopulation in subsequent sessions, where a session starts with a new assignment of metaheuristics and ends when termination criteria for the session are satisfied. Individual agents have their local archives of non-dominated solutions extracted in a session, while there is a global archive keeping all non-dominated solutions found so far. At the end of each session, all subpopulations are combined into one global population to be used for the initialization of the next session. Similarly, all local archives are merged with the global archive to get the set of all non-dominated solutions found by all metaheuristics through working on subsets of different rank-levels. This way, the metaheuristics cooperate with each other by sharing their search experiences through collecting them in a common population and a common global archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2009 multiobjective optimization benchmark problems set. Analysis of the experimental results demonstrated that the proposed architecture achieves better performance compared to majority of its state-of-the-art competitors in almost all problem instances.