Component-level knowledge transfer based on diffusion model in evolutionary multitasking

Evolutionary Multitasking has proven effective in addressing multi-task optimization, with knowledge transfer playing a key role in improving algorithm performance. However, existing studies mainly emphasize the timing and methods of transfer, often constrained by specific task assumptions, while overlooking the potential of components during the process. Additionally, reliance on traditional stochastic evolutionary operators limits search efficiency. To address these limitations, this paper proposes a Diffusion-based Multifactorial Evolutionary Algorithm (D-MFEA), featuring a novel component-level knowledge transfer framework for unconstrained single-objective multi-task problems. This framework integrates a diffusion model as the transfer component, enabling efficient knowledge sharing and collaboration between evolutionary and transfer components. It demonstrates strong generalization, seamlessly adapting to and enhancing various MFEA algorithms. By generating high-quality individuals, the diffusion model facilitates positive transfer, reducing reliance on stochastic evolutionary operators and assumptions about task relationships, thereby significantly improving the efficiency of knowledge transfer. Theoretical analyses ensure the diffusion model’s ability to generate high-quality individuals, while experiments on multiple single-objective multi-task benchmarks and a real-world application demonstrate that D-MFEA achieves faster convergence. Ablation studies confirm the effectiveness and robustness of the framework’s components and analyze the impact of varying noise configurations. Extensive results show that our algorithm outperforms state-of-the-art methods.