基于单纯形梯度的多起点全局优化算法

摘要

用已知样本点信息构造单纯形梯度及插值函数，提出一种基于单纯形梯度的局部搜索算法。该算法结合有效样本点集Ω的混合选取策略，改进了多起点聚类全局优化算法。结果表明，新算法在效率和稳定性方面均有较大改进，并可有效处理原算法针对“窄谷”类函数估值次数过高的问题。%Using the information of known sample points to construct simplex gradient and interpolation function,we proposed a local search algorithm based on the simplex gradient.The algorithm combined the effective sample point set Ω with a hybrid selection strategy,and improved the multistart clustering global optimization algorithm (GLOBAL).Experimental results show that the new algorithm has great improvement in efficiency and stability,and can effectively deal with the problem of high evaluation of the original algorithm for the “narrow valley”function.