In typical black-box optimization applications, the available computational budget is often allocated to a single algorithm, typically chosen based on user preference with limited knowledge about the problem at hand or according to some expert knowledge. However, we show that splitting the budget across several algorithms yield significantly better results. This approach benefits from both algorithm complementarity across diverse problems and variance reduction within individual functions, and shows that algorithm portfolios do NOT require parallel evaluation capabilities. To demonstrate the advantage of sequential algorithm portfolios, we apply it to the COCO data archive, using over 200 algorithms evaluated on the BBOB test suite. The proposed sequential portfolios consistently outperform single-algorithm baselines, achieving relative performance gains of over 14%, and offering new insights into restart mechanisms and potential for warm-started execution strategies.

翻译：在典型的黑盒优化应用中，可用的计算资源通常被分配给单一算法，其选择往往基于用户对问题认知有限的偏好或特定专家经验。然而，本研究表明：将计算预算分配给多个算法能显著改善优化效果。该方法既能利用不同算法在多样化问题上的互补性，又能降低单一函数优化结果的方差，同时证明算法组合并不依赖并行计算能力。为验证顺序算法组合的优势，我们将其应用于COCO数据档案库，使用超过200种算法在BBOB测试集上进行评估。所提出的顺序算法组合始终优于单算法基线，实现了超过14%的相对性能提升，并为重启机制与热启动执行策略的潜力提供了新的见解。