论文标题
一种简单的新型全球优化算法及其在某些基准功能上的性能
A Simple Novel Global Optimization Algorithm and Its Performance on Some Benchmark Functions
论文作者
论文摘要
本文提出了一项新的框架工作，以查找全球最小值，我们将其称为“剪切优化”。在每次迭代中，它从可行区域中获取一些样本，并在这些点上评估目标函数。基于观察结果，它从可行区域切断了一个不太可能包含全球最小值的子区域。然后将其重复使用可行区域，被其余区域代替，直到其余区域足够``小''。如果将全局最小值保留在每个迭代的其余区域，则可以以任意精度定位。鉴于其简单形式，该框架工作非常有效，并且可以应用于黑框功能，因为不需要特殊的结构和衍生信息。在某些基准功能上评估了拟议的框架工作的性能，结果表明它可以很快找到全球最小值。
This paper propose a new frame work for finding global minima which we call optimization by cut. In each iteration, it takes some samples from the feasible region and evaluates the objective function at these points. Based on the observations it cuts off from the feasible region a subregion that is unlikely to contain a global minimum. The procedure is then repeated with the feasible region replaced by the remaining region until the remaining region is ``small'' enough. If a global minimum is kept in the remaining region of each iteration, then it can be located with an arbitrary precision. The frame work is surprisingly efficient in view of its simple form and can be applied to black-box functions since neither special structure nor derivative information is required. The performance of the proposed frame work is evaluated on some benchmark functions and the results show that it can find a global minimum rather quickly.
