实在是坚持不下去写CSDN 还是回家记录好啊哈哈 太温馨啦

坚持努力！不能摆烂三年！即使什么都研究不出来…哭了

群智能优化算法笔记Part1

教与学算法背景

教与学优化（Teaching-Learning Based Optimization,TLBO） 由印度学者Rao等2011年提出 原理说白了就是模仿学校找好老师教学生，学生请教好学生取长补短的过程，不断迭代优化，最终得到一个良好的结果。

教与学优化算法实现

import copy
import random

import numpy as np
import math

def initialization(pop, ub, lb, dim):
    """初始化函数"""
    """
    pop 种群数量
    dim 个体维度
    ub 上边界
    lb 下边界
    X 输出的总群
    """
    X = np.zeros([pop, dim])
    for i in range(pop):
        for j in range(dim):
            X[i, j] = (ub[j] - lb[j]) * np.random.random() + lb[j]
    return X

def BorderCheck(X, ub, lb, pop, dim):
    """边界检查函数"""
    for i in range(pop):
        for j in range(dim):
            if X[i, j] > ub[j]:
                X[i, j] = ub[j]
            if X[i, j] < lb[j]:
                X[i, j] = lb[j]
    return X

def CaculateFitness(X, fun):
    """计算种群的所有个体的适应度值"""
    pop = X.shape[0]
    fitness = np.zeros([pop, 1])
    for i in range(pop):
        fitness[i] = fun(X[i, :])
    return fitness

def SortFitness(Fit):
    """对适应度进行排序"""
    fitness = np.sort(Fit, axis=0)  # 按列排序
    index = np.argsort(Fit, axis=0)  # 索引
    return fitness, index

def SortPosition(X, index):
    """根据适应度值的大小对个体位置进行排序"""
    Xnew = np.zeros(X.shape)
    for i in range(X.shape[0]):
        Xnew[i, :] = X[index[i], :]
    return Xnew

def TLBO(pop,dim,lb,ub,maxIter,fun):
    """教与学"""
    X=initialization(pop,ub,lb,dim)
    fitness=CaculateFitness(X,fun)
    GbestScore=np.min(fitness)
    indexBest=np.argmin(fitness)
    GbestPosition=np.zeros([1,dim])
    GbestPosition[0,:]=copy.copy(X[indexBest,:])
    Curve=np.zeros([maxIter,1])
    for t in range(maxIter):
        print('第'+str(t),'次迭代')
        for i in range(pop):
            #教阶段
            Xmean=np.mean(X)
            indexBest=np.argmin(fitness)
            Xteacher=copy.copy(X[indexBest,:])
            beta=random.randint(0,1)
            Xnew=X[i,:]+np.random.random(dim)*(Xteacher-beta*Xmean)

            #边界检查
            for j in range(dim):
                if Xnew[j]>ub[j]:
                    Xnew[j]=ub[j]
                if Xnew[j]<lb[j]:
                    Xnew[j]=lb[j]
            #计算适应度值
            fitnessNew=fun(Xnew)
            #位置更新
            if fitnessNew<fitness[i]:
                X[i,:]=copy.copy(Xnew)
                fitness[i]=copy.copy(fitnessNew)
            #学阶段
            p=random.randint(0,dim-1)
            while i==p:
                p=random.randint(0,dim-1)
            #学阶段位置更新
            if fitness[i]<fitness[p]:
                Xnew=X[i,:]+np.random.random(dim)*(X[i,:]-X[p,:])
            else:
                Xnew = X[i, :] - np.random.random(dim) * (X[i, :] - X[p, :])
            #边界检查
            for j in range(dim):
                if Xnew[j]>ub[j]:
                    Xnew[j]=ub[j]
                if Xnew[j]<lb[j]:
                    Xnew[j]=lb[j]
            #若位置更优 更新先前解
            fitnessNew=fun(Xnew)
            if fitnessNew<fitness[i]:
                X[i,:]=copy.copy(Xnew)
                fitness[i]=fitnessNew

        fitness=CaculateFitness(X,fun)
        indexBest=np.argmin(fitness)
        if fitness[indexBest]<=GbestScore:
            GbestScore=copy.copy(fitness[indexBest])
            GbestPosition[0,:]=copy.copy(X[indexBest,:])
        Curve[t]=GbestScore

    return GbestScore,GbestPosition,Curve

解决寻找最小值问题

#适应度函数二元方程 dim也要2维
def fun(X):
    O=X[0]**2+X[1]**2
    return O

适应度值收敛结果

特殊的：适应度即为目标解 此刻目标函数最小值均为0 所以适应度值收敛 对应不同问题 目标的最优适应度值也不一样 因此要根据情况而定 切记 不要认为都收敛为0

坚持打卡 莉可镇楼