Multifactorial Evolutionary Algorithm Enhanced with Cross-task Search Direction

Recently, the multifactorial evolutionary algorithm (MFEA) has achieved remarkable success in multi-task optimization (MTO) and received extensive attention from academia and industry. The key idea of MFEA is to use the inter-task knowledge transfer to produce the mutual promotion effect of all tasks. However, MFEA still has some limitations in accelerating convergence and enhancing global search ability, especially when the optima of different optimization tasks are far away. To relieve this issue, this paper integrates a new cross-task knowledge transfer, which is based on a search direction instead of an individual. The proposed knowledge transfer strategy generates offspring by the sum of an elite individual of one task and a difference vector from another task. As a basic vector, the elite individual is used to speed up the population convergence. Adding the elite individual with a difference vector from another task can enhance the search diversity. The experimental studies have shown the effectiveness and efficiency of the proposed cross-task knowledge transfer strategy, compared with the classical MFEA on a set of benchmark problems with different degrees of similarities.