基于python的狼群搜索算法WPA

import numpy as np import math import random import heapq from test_func import * import matplotlib.pyplot as plt class wpa(): def __init__(self, pop_size=50, n_dim=2, alpha=4, beta=6, w=100, lb=-1e5, ub=1e5, max_iter=300, func=None): self.pop = pop_size self.n_dim = n_dim self.func = func self.max_iter = max_iter # max iter self.alpha = alpha # 探狼比例因子 （取[n／(α+1)，n／α]之间的整数） self.beta = beta # 狼群更新比例因子 self.w = w # 距离判定因子 self.S = 1000 # 步长因子 self.number_T = np.random.randint(self.pop / (self.alpha + 1), self.pop / self.alpha) # 探狼数量 self.h = 10 # 探狼探寻方向总数 self.T_max = 30 # 最大游走次数 self.lb, self.ub = np.array(lb) * np.ones(self.n_dim), np.array(ub) * np.ones(self.n_dim) assert self.n_dim == len(self.lb) == len(self.ub), 'dim == len(lb) == len(ub) is not True' assert np.all(self.ub > self.lb), 'upper-bound must be greater than lower-bound' self.step_a = (self.ub - self.lb) / self.S # 探狼游走步长 self.step_b = 2 * self.step_a # 猛狼奔袭步长 self.step_c = self.step_a / 2 # 围攻步长 self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.pop, self.n_dim)) self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # y = f(x) for all particles self.pbest_x = self.X.copy() # personal best location of every particle in history self.pbest_y = [self.Y[i] for i in range(self.pop)] # best image of every particle in history self.gbest_x = self.pbest_x.mean(axis=0).reshape(1, -1) # global best location for all particles self.gbest_y = np.inf # global best y for all particles self.gbest_y_hist = [] # gbest_y of every iteration self.update_gbest() def update_pbest(self): ''' personal best :return: ''' # self.need_update = self.pbest_y > self.Y # print(self.need_update) # print(self.pbest_y) # print(self.Y) for i in range(len(self.Y)): if self.pbest_y[i] > self.Y[i]: self.pbest_x[i] = self.X[i] self.pbest_y[i] = self.Y[i] # self.pbest_x = np.where(self.need_update, self.X, self.pbest_x) # self.pbest_y = np.where(self.need_update, self.Y, self.pbest_y) # print(self.pbest_x) def update_gbest(self): ''' global best :return: ''' idx_min = self.pbest_y.index(min(self.pbest_y)) if self.gbest_y > self.pbest_y[idx_min]: self.gbest_x = self.X[idx_min, :].copy() self.gbest_y = self.pbest_y[idx_min] def update(self): L_Wolf_Y = np.min(self.Y) # 记录头狼的值 L_Wolf_index = np.argmin(self.Y) # 头狼在狼群(wol)f_colony_X)中下标位置 L_Wolf_X = self.X[L_Wolf_index] # 记录头狼的变量信息 L_Wolf = [L_Wolf_Y, L_Wolf_index] # 初始化探狼 T_Wolf_V = heapq.nsmallest(self.number_T, self.Y.copy()) T_Wolf = self.find_index(T_Wolf_V, self.Y) # 0:探狼在狼群中位置 1:解 # 初始化猛狼 M_Wolf_V = heapq.nlargest(self.pop - self.number_T - 1, self.Y.copy()) M_Wolf = self.find_index(M_Wolf_V, self.Y) # 0:探狼在狼群中位置 1:解 self.detective_wolf(T_Wolf, L_Wolf_Y, L_Wolf_X, L_Wolf) # 需要修改，参考一种新的群体智能算法 狼群算法> 2013.11 式（3） # 言w增大会加速算法收敛，但W过大会使得人工狼很难进入围攻行为，缺乏对猎物的精细搜索。 d_near = 0 for d in range(self.n_dim): d_near += ((1 / (1 * self.w * self.n_dim)) * (self.ub[d] - self.lb[d])) # dnear值，判定距离 self.call_followers(d_near, M_Wolf, L_Wolf_Y, L_Wolf_X, L_Wolf_index) self.siege(M_Wolf, L_Wolf_X, L_Wolf_Y, L_Wolf_index) self.live() def find_index(self, value, wolf_colony): ret = np.zeros((len(value), 2)) for i in range(0, len(value)): ret[i][0] = wolf_colony.index(value[i]) ret[i][1] = value[i] return ret def detective_wolf(self, T_Wolf, L_Wolf_Y, L_Wolf_X, L_Wolf): # 探狼开始游走 for i in range(0, T_Wolf.shape[0]): H = np.random.randint(2, self.h) # 尝试的方向 single_T_Wolf = self.X[int(T_Wolf[i][0])] # 当前探狼 optimum_value = T_Wolf[i][1] # 游走的最优解 optimum_position = single_T_Wolf.copy() # 游走最优位置 find = False # 探狼游走行为 一旦探狼发现目标 -> 开始召唤 for t in range(0, self.T_max): # 在初始位置朝H个方向试探，直到找到一个优解 for p in range(1, H + 1): single_T_Wolf_trial = single_T_Wolf.copy() # 一种新的群体智能算法 狼群算法> 2013.11 式（1） # h越大探狼搜寻得越精细但同时速度也相对较慢 # 这个部分有点像果蝇的搜素 single_T_Wolf_trial = single_T_Wolf_trial + math.sin(2 * math.pi * p / H) * self.step_a # 根据原式不需要转化类型为int # 一种基于领导者策略的狼群搜索算法> 2013.9 式（2） # single_T_Wolf_trial = single_T_Wolf_trial + np.random.uniform(-1, 1,(single_T_Wolf_trial.shape[0],)) * self.step_a single_T_Wolf_V = self.func(single_T_Wolf_trial) # [0] # 探狼转变为头狼 if L_Wolf_Y > single_T_Wolf_V: find = True L_Wolf_Y = single_T_Wolf_V # 更新头狼解 L_Wolf_X = single_T_Wolf_trial L_Wolf_index = int(T_Wolf[i][0]) # 更新头狼下标 self.X[L_Wolf_index] = single_T_Wolf_trial # 更新头狼位置参数 T_Wolf = np.delete(T_Wolf, i, axis=0) # 探狼转为头狼，发起召唤，删除探狼 break elif optimum_value > single_T_Wolf_V: optimum_value = single_T_Wolf_V optimum_position = single_T_Wolf_trial else: # print(" > 第%d只探狼完成第%d次游走,未发现猎物" % ((i + 1), (t + 1))) # 记录上次的最优位置 single_T_Wolf = optimum_position if find is True: break if find is True: print("第%d只探狼发现猎物 %f" % (i, single_T_Wolf_V)) break else: # 若游走完成探狼没找到猎物，更新所有游走过程中最优的一次位置 self.X[int(T_Wolf[i][0])] = optimum_position def call_followers(self, d_near, M_Wolf, L_Wolf_Y, L_Wolf_X, L_Wolf_index): # 召唤行为 surrounded = False # 所有猛狼进入围攻范围才能结束 while ~surrounded: ready = 0 for m in range(0, M_Wolf.shape[0]): s_m_index = int(M_Wolf[m][0]) # 猛狼在狼群中下标 single_M_Wolf = self.X[s_m_index] # 猛狼变量 d = np.abs(L_Wolf_X - single_M_Wolf) dd = np.sum(d) while d_near < dd: for d in range(self.n_dim): single_M_Wolf[d] = single_M_Wolf[d] + self.step_b[d] * (L_Wolf_X[d] - single_M_Wolf[d]) / np.abs(L_Wolf_X[d] - single_M_Wolf[d]) single_M_Wolf_V = self.func(single_M_Wolf) # 更新猛狼位置 self.X[s_m_index] = single_M_Wolf M_Wolf[m][1] = single_M