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A topology-aware direction-adaptive multitask evolutionary algorithm
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AI
DOI：10.1016/j.swevo.2026.102497.png)
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Abstract
En 中文
In evolutionary multitask optimization, search-direction-based knowledge transfer has attracted increasing attention because it can exploit directional information generated during evolution to promote cross-task collaboration. Although such methods can effectively use search information generated during evolution, existing designs still have limitations in transfer triggering, direction reliability, and coordinate alignment. Therefore, directional information from other tasks may be imposed on target individuals that are not yet suitable to receive it, and the received information may be ineffective for these individuals. To address the above problems, this paper proposes a topology-aware direction-adaptive multitask evolutionary algorithm (TADA-MTEA). First, an adaptive local aggregation assessment mechanism is developed to screen target individuals suitable for receiving cross-task information. It uses the shared-nearest-neighbor measure to quantify neighborhood compactness and dynamically adjusts the compactness criterion according to the cross-task transfer success rate, thereby determining whether each target individual is suitable for receiving cross-task information. Second, an evolutionary direction fusion and alignment mechanism is constructed to transform directional information from other tasks into reliable transfer directions adapted to the target individual. According to the reliability of the incoming directional information, it adaptively fuses the gradient-guided direction with the local best-guided differential vector, and then maps the fused direction to the target local coordinate system using the rotation estimated by orthogonal Procrustes analysis. Finally, experimental results on benchmark suites and real-world cases show that TADA-MTEA achieves competitive and robust performance across different multitask scenarios.
Keywords:
Evolutionary multitask optimization
Search direction
Knowledge transfer
Differential evolution
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