论文标题
结合跨熵和MADS方法，以限制全局优化
Combining Cross Entropy and MADS methods for inequality constrained global optimization
论文作者
论文摘要
本文提出了一种将网格自适应直接搜索（MADS）算法与跨凝胶（CE）方法相结合的方法，以进行非平滑约束优化。 CE方法被MADS算法用作搜索步骤。这种组合的结果保留了MAD的收敛性，并允许进行有效的探索，以便远离局部最小值。 CE方法根据多元正态分布采样试验点，其平均值和标准偏差是根据到目前为止的最佳点计算得出的。数值实验显示了该方法到达可行区域并逃脱局部最小值的重要改进。
This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with the Cross-Entropy (CE) method for non smooth constrained optimization. The CE method is used as a Search step by the MADS algorithm. The result of this combination retains the convergence properties of MADS and allows an efficient exploration in order to move away from local minima. The CE method samples trial points according to a multivariate normal distribution whose mean and standard deviation are calculated from the best points found so far. Numerical experiments show an important improvement of this method to reach the feasible region and to escape local minima.
