This article presents an adaptive multi-surrogate constrained optimization method (AMSCOM) that can automatically determine the appropriate metamodel for each black-box function in the constrained optimization problem (COP) and concurrently find the optimum. In AMSCOM, each black-box function is approximated initially by several different types of candidate surrogates. Then, as optimization progresses, the poorly performing candidate surrogates of each black-box function are gradually eliminated until the appropriate surrogate is found. Meanwhile, as more than one candidate surrogate exists for each unknown function in the optimization process, multiple approximate optimization problems (AOPs) can be constructed, and new samples can be obtained by solving these AOPs. Additionally, we employ the genetic operator and the local-linear approximation–Voronoi method to generate new samples. To verify the effectiveness and investigate several properties of AMSCOM, the proposed method is tested on 12 benchmark COPs and compared with several single surrogate-based methods. Furthermore, AMSCOM is compared with several published surrogate-based constrained optimization methods, and the results further prove the superior performance of AMSCOM. The proposed method is then employed to optimize the shaft-clinching process of wheel-hub-bearing units, and a desirable result is achieved.

中文翻译：

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自适应多代理约束优化方法及其应用

本文提出了一种自适应多代理约束优化方法 (AMSCOM)，该方法可以为约束优化问题 (COP) 中的每个黑盒函数自动确定合适的元模型，并同时找到最优解。在 AMSCOM 中，每个黑盒函数最初都由几种不同类型的候选代理近似。然后，随着优化的进行，每个黑盒函数的表现不佳的候选代理被逐渐消除，直到找到合适的代理。同时，由于优化过程中每个未知函数都存在多个候选代理，因此可以构建多个近似优化问题（AOP），通过求解这些AOP可以获得新的样本。此外，我们采用遗传算子和局部线性逼近-Voronoi 方法来生成新样本。为了验证 AMSCOM 的有效性和研究 AMSCOM 的几个特性，所提出的方法在 12 个基准 COP 上进行了测试，并与几个基于单一代理的方法进行了比较。此外，将 AMSCOM 与几种已发表的基于代理的约束优化方法进行了比较，结果进一步证明了 AMSCOM 的优越性能。然后采用所提出的方法来优化轮毂轴承单元的咬轴过程，并取得了理想的结果。AMSCOM 与几种已发表的基于代理的约束优化方法进行了比较，结果进一步证明了 AMSCOM 的优越性能。然后采用所提出的方法来优化轮毂轴承单元的咬轴过程，并取得了理想的结果。AMSCOM 与几种已发表的基于代理的约束优化方法进行了比较，结果进一步证明了 AMSCOM 的优越性能。然后采用所提出的方法来优化轮毂轴承单元的咬轴过程，并取得了理想的结果。