The global minimum point of an optimization problem is of interest in engineering fields and it is difficult to be solved, especially for a nonconvex large-scale optimization problem. In this article, we consider a new memetic algorithm for this problem. That is to say, we use the determined points (the stationary points of the function) as the initial seeds of the evolutionary algorithm, other than the random initial seeds of the known evolutionary algorithms. We compare it with the multi-start method (the built-in subroutine GlobalSearch.m of the MATLAB R2020a environment), the branch-and-bound method (Couenne of the state-of-the-art open-source solver for mixed integer nonlinear programming problems), and two representative derivative-free algorithms (CMA-ES and MCS), respectively. Numerical results show that the proposed method performs well for the large-scale global optimization problems, especially the problems of which are difficult to be solved by the known global optimization methods.

翻译：优化问题的全球最低点对工程领域感兴趣,很难解决,特别是对于非电流的大规模优化问题。在本篇文章中,我们考虑对此问题采用一种新的计量算法。也就是说,我们使用确定点(功能的固定点)作为进化算法的初始种子,而不是已知演化算法的随机初始种子。我们把它与多启动方法(MATLAB R2020a环境的内置子例程.m)、分支和流动方法(混合整流非线性编程问题最先进的开放源码解析器的库)以及两个具有代表性的无衍生物算法(CMA-ES和MCS)进行了比较。数字结果显示,拟议的方法对于大规模全球优化问题来说效果良好,特别是难以通过已知的全球优化方法加以解决的问题。