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佳点集初始化种群

开发技术 开发技术 2周前 (04-30) 9次浏览
% Good Node Set Matalb-code
function [GD] = Goodnode(M,N) % M is the number of points; N is the dimension if (nargin==0) M=100; N=2; end %% tmp1 = [1: M]'*ones(1, N); Ind = [1: N]; prime1 = primes(100*N); [p,q]=find(prime1 >= (2*N+3)); tmp2 = (2*pi.*Ind)/prime1(1,q(1)); tmp2 = 2*cos(tmp2); tmp2 = ones(M,1)*tmp2; GD = tmp1.*tmp2; GD = mod(GD,1); %% For debuging plot(GD(:,1),GD(:,2),'*'); end

佳点集初始化种群佳点集初始化种群

from matplotlib import pyplot as plt
import numpy as np
import sympy


def GoodNode(m, d, lb, ub):
    """
    :param m: 点的数目
    :param d: 点的维数
    :param lb: 下界
    :param ub: 上界
    """
    temp1 = np.arange(1, m + 1).reshape(-1, 1) * np.ones((1, d))
    ind = np.arange(1, d + 1)
    prime = list(sympy.sieve.primerange(0, 100 * d))
    idx = np.where(np.array(prime) >= (2 * d + 3))[0]
    temp2 = (2 * np.pi * ind) / prime[idx[1]]
    temp2 = 2 * np.cos(temp2)
    temp2 = np.ones((m, 1)) * temp2
    gd = temp1 * temp2
    gd = np.mod(gd, 1)
    pop = lb + gd*(ub - lb)
    return pop


if __name__ == "__main__":
    p = GoodNode(100, 2, -100, 100)
    plt.figure()
    plt.scatter(p[:, 0], p[:, 1], marker="*")

    p1 = -100 + np.random.rand(100, 2) * 200
    plt.figure()
    plt.scatter(p1[:, 0], p1[:, 1], marker=".")
    plt.show()

佳点集初始化种群佳点集初始化种群


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