通过重构计算方式，让进化迁移优化在多任务场景下提速近百倍。
Towards Efficient Evaluation of Evolutionary Transfer Optimization: Case Studies on Task-Parameterized Applications
- 将任务参数化应用中的串行计算改写为可并行的矩阵形式。
- 在机械臂和轨迹评估中分别实现256倍和93倍的加速。
- 适合需要高效多任务优化的研究者和工程开发者。
随着进化迁移优化（ETO）应用于更大规模的相关任务集合，问题评估成为主要的运行时开销来源。本文研究了任务参数化应用中的问题侧评估扩展性，将特定应用的串行计算重构为适合并行执行的形式。评估扩展性被分为两个层面：评估任务的数量和每个任务内的工作负载。在多任务优化中，采用累积连杆方向的累加矩阵表示，重构了矩阵递归的运动学臂评估；在顺序迁移优化中，通过轨迹与碰撞计算的混合矩阵表示，重构了逐点B样条轨迹评估。两种重构方法均保持与参考评估相近的数值精度，显著降低运行时间，分别实现256.72倍和93.91倍的端到端加速。结果表明，问题侧重构是实现可扩展ETO的有效路径。相关应用实现与实验脚本已开源，支持可复现性和再利用。
原文摘要 · Abstract (English)
As evolutionary transfer optimization (ETO) scales to larger collections of related tasks, problem evaluation can become a major source of runtime growth. This work studies problem-side evaluation scaling in task-parameterized applications and reformulates application-specific serial computations into forms suitable for parallel execution. We organize evaluation scaling into two levels: the number of evaluated tasks and the workload within each task. In multi-task optimization, matrix-recursive kinematic-arm evaluation is reformulated using an accumulation-matrix representation of cumulative link directions. In sequential transfer optimization, pointwise B-spline trajectory evaluation is reformulated using a blending-matrix representation for trajectory and collision computations. Both reformulations maintain close numerical agreement with their reference evaluations and substantially reduce runtime, yielding $256.72\times$ and $93.91\times$ end-to-end speedups, respectively. These results demonstrate problem-side reformulation as a practical route toward scalable ETO. Both application implementations and experimental scripts are released as open source to support reproducibility and reuse.
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