一种面向联合机会约束噪声优化的置信驱动进化算法

中文摘要 AI 辅助

许多现实世界的优化问题涉及噪声目标评估和概率约束，尤其是联合机会约束，其评估计算成本高昂。在本工作中，我们提出CR-EA-C，一种置信驱动的进化算法，用于求解联合机会约束下的噪声黑箱优化问题。CR-EA-C引入三个关键组件：（1）联合机会约束的解析可行性估计，（2）用于噪声下鲁棒比较的成对统计排序机制，以及（3）加速收敛的改进不可行性驱动生存策略。这些组件在提高函数评估效率的同时，实现了统计上可靠的决策。所提方法在多种不确定性分布下与四种近期元启发式算法进行了评估。此外，其实际有效性还在两个额外的现实世界优化问题上进行了评估，并与传统静态采样方法进行了比较。实验结果表明，CR-EA-C始终满足规定的联合机会约束，同时整体上取得了有竞争力的目标值。这证明了CR-EA-C是噪声优化的一种有效的通用方法。

英文摘要

Many real-world optimization problems involve noisy objective evaluations and probabilistic constraints, particularly in the form of joint chance constraints, which are computationally expensive to evaluate. In this work, we propose CR-EA-C, a confidence-driven evolutionary algorithm for solving noisy black-box optimization problems under joint chance constraints. CR-EA-C introduces three key components: (1) analytical feasibility estimation for joint chance constraints, (2) a pairwise statistical ranking mechanism for robust comparison under noise, and (3) a modified infeasibility-driven survival strategy to accelerate convergence. These components enable statistically reliable decision-making while improving the efficiency of function evaluations. The proposed method is evaluated against four recent metaheuristic algorithms under various uncertainty distributions. Furthermore, its practical effectiveness is also assessed on two additional real-world optimization problems and compared with conventional static sampling methods. Experimental results show that CR-EA-C consistently satisfies the prescribed joint chance constraints while achieving competitive objective values overall. This demonstrates that CR-EA-C is an effective general-purpose approach for noisy optimization.

发表机构

• School of Engineering and Technology, The University of New South Wales（新南威尔士大学工程学院与技术学院）

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