Evolutionary Multitask Optimization With Lower Confidence Bound-Based Solution Selection Strategy

Zhenzhong Wang, Lulu Cao, Liang Feng, Min Jiang, Kay Chen Tan

Abstract: Evolutionary multitasking (EMT) is an emerging research direction within the evolutionary computation community, attempting to concurrently solve multiple optimization tasks by exploiting the underlying synergies between the tasks. Recently, numerous explicit transfer strategies have been developed for enhancing positive transfer among optimization tasks. Nevertheless, most of these methods conduct knowledge transfer by transferring the best solutions from a source task to the target task, while ignoring the proper use of information from the target task in solution selection. As a result, the transferred solutions could not well adapt to the target task, thus limiting the effectiveness of knowledge transfer across tasks. To address this issue, this article proposes a solution selection method based on the lower confidence bound (LCB) for EMT, which is designed by leveraging task-specific information of both source and target tasks. With the proposed LCB metric, a number of high-quality solutions that could be more helpful for the target task can be selected and transferred to enhance positive transfer in EMT. To verify the effectiveness of the proposed approach, the solution selection method is embedded into several existing EMT algorithms and then evaluated on the single-objective multitasking benchmarks, the multiobjective multitasking benchmark, and a real-world application. The obtained results confirmed the generality and efficacy of the proposed solution selection approach.