A similarity-aware multifactorial subpopulation multitask evolutionary algorithm

Effective knowledge transfer across interrelated tasks remains a core challenge in multi-objective evolutionary multitask optimization, especially when task similarity is heterogeneous across regions of the search space. This paper proposes the Similarity-aware, Multifactorial, Subpopulation-based Evolutionary Algorithm (SMS-MFEA), which extends the MFEA framework with a similarity-driven transfer mechanism at the subpopulation level. By employing K-means clustering to partition populations into subpopulations, SMS-MFEA quantifies inter-task similarity through maximum mean discrepancy to identify transferable knowledge domains. The strategy incorporates an adaptive three-strategy selection mechanism that determines optimal knowledge transfer approaches based on quantified task similarities. This integration enhances matching efficiency, improves shared knowledge utilization, and accelerates population evolution. The strategy ensures effective knowledge sharing for strongly correlated tasks while mitigating negative transfer between dissimilar tasks. Experimental validation demonstrates the method’s efficacy, with the algorithm achieving optimal inverted generational distance values in 12 cases and optimal hypervolume values in 13 instances across 18 evaluations from nine established multi-objective multitasking benchmark functions, thereby confirming its operational efficiency.