Multiple Penalties and Multiple Local Surrogates for Expensive Constrained Optimization
提出一种进化算法MPMLS，通过定义多个子问题（每个子问题使用不同惩罚系数和局部代理模型）来求解昂贵的约束优化问题，能保持种群多样性并降低建模开销。
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.