A multi-task evolutionary algorithm for solving the problem of transfer targets

Abstract

In recent years, multi-task optimisation, aimed at handling multiple optimisation problems simultaneously, has received great attention in the field of evolutionary algorithms. Research on multi-task evolutionary algorithms mostly focuses on solving the problem of effective transfer, which usually transfers the extracted effective knowledge to the matching selection stage or environment selection stage. However, the impact of these transfer targets on algorithm performance is rarely studied. To solve the problem of transfer targets, we propose a multi-task evolutionary algorithm with a hybrid knowledge transfer strategy (MTEA-HKTS). Firstly, we determined three transfer targets and designed knowledge transfer strategies for each stage by analysing the population changes in the differential evolution algorithm. Secondly, a knowledge transfer strategy is devised to control the transfer targets. Finally, we designed a new archive update strategy that uses a distribution model constructed for recent generations of populations to calculate gene similarity. The experimental results on the multi-task benchmark problems show that the transfer targets affect the performance of the multi-task optimisation algorithm, and verify the superiority of the proposed MTEA-HKTS algorithm.