This article proposes an evolutionary algorithm using multiple penalties and multiple local surrogates (MPMLS) for expensive constrained optimization. In each generation, MPMLS defines and optimizes a number of subproblems. Each subproblem penalizes the constraints in the original problem using a different penalty coefficient and has its own search subregion. A local surrogate is built for optimizing each subproblem. Two major advantages of MPMLS are: 1) it can maintain good population diversity so that the search can approach the optimal solution of the original problem from different directions and 2) it only needs to build local surrogates so that the computational overhead of the model building can be reduced. Numerical experiments demonstrate that our proposed algorithm performs much better than some other state-of-the-art evolutionary algorithms.

中文翻译：

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昂贵的约束优化的多重惩罚和多重局部代理

本文提出了一种使用多重惩罚和多重局部代理 (MPMLS) 的进化算法，用于代价高昂的约束优化。在每一代中，MPMLS 定义并优化了许多子问题。每个子问题使用不同的惩罚系数惩罚原始问题中的约束，并有自己的搜索子区域。建立一个局部代理来优化每个子问题。MPMLS 的两大优点是：1) 可以保持良好的种群多样性，使搜索可以从不同方向逼近原问题的最优解；2) 只需要构建局部代理，从而减少模型构建的计算开销可以减少。