# Offline Multi-Objective Optimization Source: [Offline Multi-Objective Optimization]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:52:14.378Z Authors: Ke Xue, Rong-Xi Tan, Xiaobin Huang, Chao Qian Published: 2024-06-06T03:35:09.000Z ## Abstract Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. In addition to the objective function being black-box and expensive to evaluate, numerous complex real-world problems entail optimizing multiple conflicting objectives, i.e., multi-objective optimization (MOO). Nevertheless, offline MOO has not progressed as much as offline single-objective optimization (SOO), mainly due to the lack of benchmarks like Design-Bench for SOO. To bridge this gap, we propose a first benchmark for offline MOO, covering a range of problems from synthetic to real-world tasks. This benchmark provides tasks, datasets, and open-source examples, which can serve as a foundation for method comparisons and advancements in offline MOO. Furthermore, we analyze how the current related methods can be adapted to offline MOO from four fundamental perspectives, including data, model architecture, learning algorithm, and search algorithm. Empirical results show improvements over the best value of the training set, demonstrating the effectiveness of offline MOO methods. As no particular method stands out significantly, there is still an open challenge in further enhancing the effectiveness of offline MOO. We finally discuss future challenges for offline MOO, with the hope of shedding some light on this emerging field. Our code is available at \\url{https://github.com/lamda-bbo/offline-moo}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Offline Multi-Objective Optimization on Off-MOO-Bench MO-NAS](): Offline Multi-objective Optimization; Off-MOO-Bench MO-NAS; Average IGDoffline Rank: 3.89. - [Offline Multi-Objective Optimization on Off-MOO-Bench MO-NAS (50th percentile)](): Offline Multi-objective Optimization; Off-MOO-Bench MO-NAS (50th percentile); Average HV Rank: 4.21. - [Offline Multi-Objective Optimization on Off-MOO-Bench MORL](): Offline Multi-objective Optimization; Off-MOO-Bench MORL; Average IGDoffline Rank: 2.6. - [Offline Multi-Objective Optimization on Off-MOO-Bench Synthetic](): Offline Multi-objective Optimization; Off-MOO-Bench Synthetic; Average IGDoffline Rank: 5.75. - [Offline Multi-Objective Optimization on Off-MOO-Bench Synthetic (50th percentile)](): Offline Multi-objective Optimization; Off-MOO-Bench Synthetic (50th percentile); Average HV Rank: 5.24. - [Offline Multi-Objective Optimization on Off-MOO-Bench RE (50th percentile)](): Offline Multi-objective Optimization; Off-MOO-Bench RE (50th percentile); Average HV Rank: 5.82. - [Offline Multi-Objective Optimization on Synthetic](): Offline Multi-objective Optimization; Synthetic; Average IGDoffline Rank: 6.58. - [Offline Multi-Objective Optimization on RE](): Offline Multi-objective Optimization; RE; Average IGDoffline Rank: 5.29. - [Offline Multi-Objective Optimization on Off-MOO-Bench Sci-Design](): Offline Multi-objective Optimization; Off-MOO-Bench Sci-Design; Average IGDoffline Rank: 4.95. - [Offline Multi-Objective Optimization on Off-MOO-Bench](): Offline Multi-objective Optimization; Off-MOO-Bench; Avg Rank (Overall): 6.41. - [Offline Multi-Objective Optimization on Off-MOO-Bench MORL (50th percentile)](): Offline Multi-objective Optimization; Off-MOO-Bench MORL 50th percentile; Average HV Rank: 9.3. - [Offline Multi-Objective Optimization on Off-MOO-Bench Sci-Design (50th percentile)](): Offline Multi-objective Optimization; Off-MOO-Bench Sci-Design (50th percentile); Average HV Rank: 7.88. - [Offline Multi-Objective Optimization on Off-MOO-Bench RE](): Offline Multi-objective Optimization; Off-MOO-Bench RE; Average IGDoffline Rank: 7.71. ## Original sources and implementation [Original paper]() [PDF]()