Constrained optimization problems (COPs) are frequently encountered in real-world design applications. For some COPs, the evaluation of the objective(s) and/or constraint(s) may involve significant computational/temporal/financial cost. Such problems are referred to as expensive COPs (ECOPs). Surrogate modeling has been widely used in conjunction with optimization methods for such problems, wherein the search is partially driven by an approximate function instead of true expensive evaluations. However, for any true evaluation, nearly all existing methods compute all objective and constraint values together as one batch. Such full evaluation approaches may be inefficient for cases where the objective/constraint(s) can be evaluated independently of each other. In this article, we propose and study a constraint handling strategy for ECOPs using partial evaluations. The constraints are evaluated in a sequence determined based on their likelihood of being violated; and the evaluation is aborted if a constraint violation is encountered. Modified ranking strategies are introduced to effectively rank the solutions using the limited information thus obtained, while saving on significant function evaluations. The proposed algorithm is compared with a number of its variants to establish the utility of its key components systematically. Numerical experiments and benchmarking are conducted on a range of mathematical and engineering design problems to demonstrate the efficacy of the approach compared to state-of-the-art evolutionary optimization approaches.

中文翻译：

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昂贵的进化约束优化的部分评估策略


约束优化问题 (COP) 在实际设计应用中经常遇到。对于某些 COP，目标和/或约束的评估可能涉及大量的计算/时间/财务成本。此类问题被称为昂贵的 COP (ECOP)。代理建模已广泛与此类问题的优化方法结合使用，其中搜索部分由近似函数驱动，而不是真正昂贵的评估。然而，对于任何真正的评估，几乎所有现有方法都将所有目标值和约束值作为一批一起计算。对于可以彼此独立评估目标/约束的情况，这种全面评估方法可能效率低下。在本文中，我们提出并研究了一种使用部分评估的 ECOP 约束处理策略。根据约束被违反的可能性确定的顺序对约束进行评估；如果遇到约束违规，则评估将中止。引入改进的排序策略，以使用由此获得的有限信息对解决方案进行有效排序，同时节省重要的功能评估。将所提出的算法与其许多变体进行比较，以系统地确定其关键组件的实用性。对一系列数学和工程设计问题进行了数值实验和基准测试，以证明该方法与最先进的进化优化方法相比的有效性。