Operational decomposition for large scale multi-objective optimization problems
Luis Miguel Antonio, Carlos A. Coello Coello, Silvia B. González-Brambila, Josué Figueroa González, Guadalupe Castillo Tapia · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
Most multi-objective evolutionary algorithms (MOEAs) of the state of the art treat the decision variables of a multi-objective optimization problem (MOP) as a whole. However, when dealing with MOPs with a large number of decision variables (more than 100) their efficacy decreases as the number of decision variables of the MOP increases. Problem decomposition, in terms of decision variables, has been found to be extremely efficient and effective for solving large scale optimization problems. In this work, we study the effect of what we call "operational decomposition", which is a novel framework based on coevolutionary concepts to apply MOEAs's crossover operator without adding any extra cost. We investigate the improvements that NSGA-III can achieve when combined with operational decomposition. This new scheme is capable of improving efficiency of a MOEA when dealing with large scale MOPs having from 200 up to 1200 decision variables.