Combining Cross-Entropy and MADS Methods for Inequality Constrained Global Optimization

This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with Cross-Entropy (CE) method for nonsmooth constrained optimization. The CE is used as an exploration step by MADS algorithm. result of this combination retains convergence properties and allows efficient in order move away from local minima. samples trial points according multivariate normal distribution whose mean standard deviation are calculated best found so far. Numerical experiments show efficiency compared other global optimization heuristics. Moreover, applied on complex engineering test problems, important improvement reach feasible region escape