求解全局优化问题的一个新的填充函数算法

A new filled function method for global optimization

构造了一个新的单参数且连续可微的填充函数,并将其与进化算法相结合提出了一个新的填充函数算法。该算法通过不断跳出局部最优解进入更优解所在区域的方式来提高优化效率,通过设置进化算法中种群均匀分布、增加种群多样性的方式增加了算法的全局寻优性能,并将该算法在标准测试集上进行了测试。结果表明,该算法简单有效,并且随着优化问题维度的提高而表现稳定。

A new hybrid single-parameter filled function is proposed which is also continuous and differentiable.Combined with an evolutionary algorithm,a new filled function algorithm is proposed.The new filled function algorithm can improve the efficiency of the optimization by repeatedly escaping from current local optimum to better areas with better solutions.To enhance the explore ability of the proposed algorithm,we use uniform distribution to make better population diversity.Numerical experiments show the simplicity and efficiency of the proposed algorithm.