Periodical generation update using an unbounded external archive for multi-objective optimization

In the evolutionary multi-objective optimization (EMO) community, an unbounded external archive has been used in some studies for evaluating the performance of EMO algorithms. Those studies show that the unbounded external archive often includes better solutions than the final population. Thus, it is likely that the search ability of an EMO algorithm can be improved by periodically updating the current population using the unbounded external archive (i.e., by periodically choosing good solutions from all the examined solutions as the current population). However, the usefulness of such a global generation update scheme has not been studied in the literature. In this paper, we examine the effect of the periodical global generation update on the performance of well-known and frequently-used EMO algorithms: NSGA-II, MOEA/D and NSGA-III. We use the PBI function with uniformly distributed weight vectors for the periodical global generation update. In our computational experiments, we obtain clearly improved results by the periodical global generation update. We also examine the effect of the frequency of the global generation update (e.g., every 20 generations) on the performance of each EMO algorithm and its run time.