Evolutionary multitasking in bi-level optimization

Evolutionary multitasking has recently emerged as an effective means of facilitating implicit genetic transfer 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 of bi-level optimization, a significant challenge surfaces from 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. Accordingly, the significance of this paper lies in showcasing that the practicality of population-based bi-level optimization can be considerably enhanced by simply incorporating the novel concept of evolutionary multitasking into the search process. As a result, it becomes possible to process multiple lower level optimization tasks concurrently, thereby facilitating the exploitation of underlying commonalities among them. To demonstrate the implications of our proposal, we present computational experiments on some synthetic benchmark functions, as well as a real-world case study in complex engineering design from the composites manufacturing industry.