Summit-assisted Evolutionary Multitasking
Cheng-Yu Hsieh, Rung-Tzuo Liaw · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
Evolutionary computation has served as a blooming research area for decades, in which evolutionary algorithms are inspired from mechanisms of evolution as well as cognitive and social behaviors of creatures in nature as searching processes. An emerging branch of evolutionary computation proposed in recent year is the evolutionary multitasking, which is to manipulate evolutionary algorithms for tackling multitask optimization problems. Recent studies of evolutionary multitasking have drawn much attention on designing effective knowledge transfer mechanisms. This study proposes a novel evolutionary multi-tasking method named summit-assisted evolutionary multitasking (SaEMT) by integrating summit-based knowledge transfer into the general multi-population evolutionary multitasking method. There are two main features in the proposed method, including the summit-based recombination, and the dynamic control of transfer rate. Empirical results show that the proposed method can outperform classical and advanced evolutionary multitasking methods in terms of solution quality and convergence speed. Experimental results also discover that the SaEMT is able to complete in acceptable running time.