Cooperative co-evolution with sensitivity analysis-based budget assignment strategy for large-scale global optimization

Abstract

Cooperative co-evolution has proven to be a successful approach for solving large-scale global optimization (LSGO) problems. These algorithms decompose the LSGO problems into several smaller subcomponents using a decomposition method, and each subcomponent of the variables is optimized by a certain optimizer. They use a simple technique, the round-robin method, to equally assign the computational time. Since the standard cooperative co-evolution algorithms allocate the computational budget equally, the performance of these algorithms deteriorates for solving LSGO problems with subcomponents by various effects on the objective function. For this reason, it could be very useful to detect the subcomponents’ effects on the objective function in LSGO problems. Sensitivity analysis methods can be employed to identify the most significant variables of a model. In this paper, we propose a cooperative co-evolution algorithm with a sensitivity analysis-based budget assignment method (SACC), which can allocate the computational time among all subcomponents based on their different effects on the objective function, accordingly. SACC is benchmarked on imbalanced LSGO problems. Simulation results confirm that SACC obtains a promising performance on the majority of the imbalanced LSGO benchmark functions.