发表机构
Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对少样本多目标多任务优化中精英解分布难以获取的瓶颈，本文提出迭代顺序迁移（IST）方法，通过顺序迁移和似然驱动的任务优先级排序，在紧凑预算下有效提升迁移效用。
AI 中文摘要
多任务优化（MTO）通过跨多个优化任务应用知识迁移，成为一种同时求解协同优化任务的有前景的方法。然而，在MTO中开发有效的知识迁移机制从根本上依赖于跨任务对齐精英解分布。这种依赖性在少样本优化场景中造成了关键瓶颈，因为受限的评估预算阻碍了识别有益迁移所需的精英解分布。在多目标多任务问题中，这一挑战更加严峻，因为每个优化器必须逼近连续的帕累托流形，而非单一最优点。本文引入迭代顺序迁移（IST）来规避这一瓶颈。我们将MTO建模为一系列顺序迁移优化问题，每次迭代将评估集中在单个目标任务上。我们提出了一种基于似然的任务优先级排序机制，通过识别最可能准备好进行知识整合的任务来最大化迁移效用。在基准问题和实际问题上进行的实证结果验证了所提方法在紧凑预算下的有效性。
英文摘要
Applying knowledge transfer across multiple optimization tasks, multitask optimization (MTO) emerges as a promising approach to solving synergistic optimization tasks simultaneously. However, the development of effective knowledge transfer mechanisms in MTO fundamentally relies on aligning elite solution distributions across tasks. This dependency creates a critical bottleneck in few-shot optimization regimes, as restricted evaluation budgets impede the identification of elite solution distributions required for beneficial transfer. This challenge is exacerbated in multiobjective multitask problems, where each optimizer must approximate a continuous Pareto manifold rather than a single optimal point. This paper introduces Iterative Sequential Transfer (IST) to circumvent this bottleneck. We model MTO as a sequence of sequential transfer optimization problems, concentrating evaluations on a single target per iteration. We propose a likelihood-informed task prioritization mechanism to maximize transfer utility by identifying the task most likely ready for knowledge integration. Empirical results on benchmark and real-world problems verify the effectiveness of the proposed method under tight budgets.
CommentsAccepted paper in WCCI/CEC 2026
