一种基于单纯形搜索的混合分布估计算法

A Hybrid Optimization Method Combining Simplex Search and Estimation of Distribution Algorithm

充分利用分布估计算法全局搜索能力强,且对问题的特殊信息依赖性较弱的特点,将分布估计算法与单纯形法结合,强化分布估计算法的局部搜索能力,提出一种基于单纯形搜索的混合分布估计算法。采用经典的测试函数对算法进行测试,证明其相比于单纯的分布估计算法的优越性。

This paper proposed a hybrid NM-EDA algorithm based on Nelder-Mead （NM） simplex search method and estimation of distribution algorithm （EDA）. EDA has a strong capability of global searching and relies little on specific optimization information, which makes it easy to combine with other methods. The NM simplex method is an optimization method with good local searching ability. The combination of EDA with NM simplex method produces faster convergence and can improve the probability of finding the optimal solution. In a group of several test function problems taken from the literature, the computational results show that the hybrid NM-EDA outperforms other two optimization methods, i.e. the standard EDA and improved EDA.