Tuning Multi-objective Multi-population Evolutionary Models for High-dimensional Problems: The Case of the Migration Process

Abstract

Multi-objective multi-population evolutionary procedures have become one of the most outstanding metaheuristics for solving problems characterized by the curse of dimensionality. A critical aspect of these models is the migration process, defined as the exchange of individuals between subpopulations every few iterations or generations, which has typically been adjusted according to a set of guidelines proposed more than 20 years ago, when the capacity to deal with problems was significantly less than it is today. However, the constant increase in computational power has made it possible to tackle today’s complex real-world problems of great interest more plausibly, but with larger populations than before. Against this background, this paper aims to study whether these classical recommendations are still valid today, when both the magnitude of the problems and the size of the population have increased considerably, considering how this adjustment affects the performance of the procedure. In addition, the increase in the population size, coupled with the fact that multi-objective optimization is being addressed, has led to the development of a novel elitist probabilistic migration strategy that considers only the Pareto front. The results show some interesting and unexpected conclusions, in which other issues, such as the number of subpopulations or their size, should be considered when fitting multi-population models. Furthermore, some of the previously mentioned classical recommendations may not be well-suited for high-dimensional problems.