测试函数2D图

1、一维函数、一维函数 一维单峰函数一维单峰函数 一维多峰单全局最优解函数一维多峰单全局最优解函数 一维多峰多局部最优解函数一维多峰多局部最优解函数 2、二维函数、二维函数 2.1 二维单峰函数二维单峰函数 2.2 二维多峰单全局最优解函数二维多峰单全局最优解函数 2.2.1 SHUBERT FUNCTION 欢迎下载2 Description: Dimensions: 2 The Shubert function has several local minima and many global minima. The second plot shows the the function on a smaller input domain, to allow for easier viewing. Input Domain: The function is usually evaluated on the square xi -10, 10, for all i = 1, 2, although this may be restricted to the square xi -5.12, 5.12, for all i = 1, 2. Global Minimum: 欢迎下载3 Schwefel Function 2.2.2 EGGHOLDER FUNCTION 欢迎下载4 Description: Dimensions: 2 The Eggholder function is a difficult function to optimize, because of the large number of local minima. Input Domain: The function is usually evaluated on the square xi -512, 512, for all i = 1, 2. Global Minimum: 欢迎下载5 2.2.3 Levy 5 test objective function. This class defines the Levy 5 global optimization problem. This is a multimodal minimization problem defined as follows: Here, represents the number of dimensions and for . Two-dimensional Levy 5 function Global optimum: for . 2.2.4 LANGERMANN FUNCTION 欢迎下载6 Description: Dimensions: d The Langermann function is multimodal, with many unevenly distributed local minima. The recommended values of m, c and A, as given by Molga a larger m leads to a more difficult search. The recommended value of m is m = 10. The functions two-dimensional form is shown in the plot above. Input Domain: The function is usually evaluated on the hypercube xi 0, , for all i = 1, , d. Global Minima: 多维多峰多局部最优解函数多维多峰多局部最优解函数 STYBLINSKI-TANG FUNCTION 欢迎下载15 Description: Dimensions: d The Styblinski-Tang function is shown here in its two-dimensional form. Input Domain: The function is usually evaluated on the hypercube xi -5, 5, for all i = 1, , d. Global Min