Evolutionary multitask optimization with online knowledge transfer and probabilistic outlier detection

The growing complexity of real-world optimization problems often necessitates the simultaneous optimization of multiple tasks. Evolutionary multitasking (EMT) algorithms, including the multifactorial evolutionary algorithm (MFEA), have gained significant attention for their ability to solve multiple related optimization tasks concurrently by transferring knowledge across tasks. However, existing EMT methods, particularly MFEA, face challenges in managing dynamic task relationships and ensuring beneficial knowledge transfer, especially in large-scale or highly heterogeneous multitask optimization problems. This paper proposes a novel method, evolutionary multitask optimization with online knowledge transfer and probabilistic outlier detection (OKTPO-MFEA), which incorporates a dynamic adjustment of the random mating probability and a probability-based outlier detection mechanism. The proposed algorithm adapts the knowledge transfer process in real-time, promoting positive knowledge transfer and suppressing negative transfer. In addition, a robust outlier detection strategy is introduced to identify and eliminate harmful genetic contributions, further enhancing optimization performance. The effectiveness of OKTPO-MFEA is validated through extensive experiments on multi-task benchmark problems, demonstrating its superiority in solution quality and convergence speed compared to state-of-the-art methods.