多目的システム共同設計のための構成的オンライン学習
Compositional Online Learning for Multi-Objective System Co-Design
複数の競合する目的を持つシステム設計問題を、部分順序を持つ機能と資源の関係としてモデル化し、少数の評価で非劣トレードオフ集合を特定するための楽観的評価子を用いた除去型リジェクトサンプリングアルゴリズムを提案した。
著者: Meshal Alharbi, Munther A. Dahleh, Gioele Zardini
分類: math.OC, eess.SY
原文アブストラクト
Many engineered systems must balance competing objectives, such as performance and safety, cost and reliability, or efficiency and sustainability, and are naturally modeled as compositions of interacting subsystems. We study online multi-objective decision-making in monotone co-design, where functionalities and resources are partially ordered, and the goal is to identify the target-feasible antichain of non-dominated trade-offs using few expensive evaluations. We introduce optimistic evaluators: history-dependent bounds on functionality and resource mappings that enable safe elimination of implementations before full evaluation. Based on these evaluators, we develop an elimination-based rejection-sampling algorithm, prove its soundness, and show that the admissible region shrinks monotonically as information accumulates. We instantiate the framework under monotonicity, Lipschitz continuity, and linear-parametric structure. For compositional co-design problems modeled by multigraphs, we show how local optimistic certificates propagate through the tractable remainder of the graph to yield system-level optimistic feasibility and resource bounds. Experiments on multi-robot fleet design, intermodal mobility systems, and synthetic monotone and Lipschitz benchmarks show substantial sample-efficiency gains over uniform sampling, Bayesian optimization, and multi-objective evolutionary algorithms.