Accelerating Evolutionary Multitasking Optimization With a Generalized GPU-Based Framework

Abstract

Evolutionary multitasking(EMT), which conducts evolutionary research on multiple tasks simultaneously, is an emerging research topic in the computation intelligence community. It aims to enhance the convergence characteristics by simultaneously conducting evolutionary research on multiple tasks, thereby facilitating knowledge transfer among tasks and achieving exceptional performance in solution quality. However, most of the existing EMT algorithms still suffer from the high computational burden especially when the number of tasks is large. To address this issue, this paper proposes a GPU-based multitasking evolutionary framework, which is able to handle thousands of tasks that arrive asynchronous in a short time. Besides, a concurrent multi-island mechanism is proposed to enable the parallel EMT algorithm to efficiently solve high-dimensional problems. Experimental results on eight problems with differing characteristics have demonstrated that the proposed framework is effective in solving high-dimensional problems and can significantly reduce the search time.