Experimental Data for the preprint "Diagonal Partitioning Strategy Using Bisection of Rectangles and a Novel Sampling Scheme"

This data is used as the basis for the following preprint: N. Guessoum et al. "Diagonal Partitioning Strategy Using Bisection of Rectangles and a Novel Sampling Scheme". Here we experimentally investigated a modification suggested to the recently introduced BIRECT (BIsection of RECTangles) algorithm. A new deterministic approach, named BIRECT-V algorithm (where V stands for vertices), combines bisection with sampling on diagonal vertices. Also, a new variation of the BIRECT-V algorithm, called BIRECT-Vl is also introduced. This data set contains the results of these experiments, the original source codes for the BIRECT-V algorithm used in the experiments, as well as the scripts used for evaluating the results would be available in a future version.
First, We applied both algorithms to several well-known test problems using from the literature, obtaining data1, data4, and data6. Second, we modified the optimization domain for certain functions, and obtained dataset 2, 3, and 5. These results were compared to the original BIRECT, BIRECT-l, DIRECT, and DIRECT-l.

本数据集为下述预印本提供了研究支撑：N. Guessoum 等. 《使用矩形二分法的对角划分策略与新型采样方案》（Diagonal Partitioning Strategy Using Bisection of Rectangles and a Novel Sampling Scheme）。本研究针对近期提出的BIRECT（BIsection of RECTangles，矩形二分法）算法的改进方案开展了实验探究。本文提出一种全新的确定性方法，命名为BIRECT-V算法（其中V代表顶点vertices），该方法将二分法与对角顶点采样相结合。此外，本文还推出了BIRECT-V算法的新型变体BIRECT-Vl。本数据集包含本次实验的全部结果、实验中所用BIRECT-V算法的原始源代码，而用于结果评估的脚本将在后续版本中对外发布。首先，我们将两款算法应用于取自公开文献的若干经典测试问题，得到数据集data1、data4与data6。其次，我们针对部分函数调整了其优化定义域，得到数据集2、3与5。最后，将本研究所得实验结果与原始BIRECT、BIRECT-l、DIRECT及DIRECT-l算法的运行结果进行了对比。