Evolutionary multitasking in bi-level optimization

Abstract

Evolutionary multitasking has recently emerged as an effective means of facilitating implicit genetic trans- fer across different optimization tasks, thereby potentially accelerating convergence characteristics for multiple tasks at once. A natural application of the paradigm is found to arise in the area of bi-level programming wherein an upper level optimization problem must take into consideration a nested lower level problem. Thus, while tackling instances ofbi-leveloptimization,asignificantchallengesurfacesfrom the fact that multiple upper level candidate solutions are to be analyzed at the same time by inferring the corresponding optimum response from the lower level. Thus, the process of bi-level optimization often becomes exorbitantly time consuming, especially in the case of real-world instances involving expensive objective function evaluations. Accord- ingly, the significance of this paper lies in showcasing that