人工蜂群算法ABC模型，python书写

import numpy as np from ypstruct import structure def run(problem, params): # 函数信息 costfunc = problem.costfunc nvar = problem.nvar varmin = problem.varmin varmax = problem.varmax # 参数信息 maxit = params.maxit npop = params.npop nonlooker = params.nonlooker limit = int(np.round(0.6*nvar*npop)) a = params.a # 空的蜂群结构 empty_bee = structure() empty_bee.position = None empty_bee.cost = None # 临时蜂群结构 newbee = structure() newbee.position = None newbee.cost = None # 初始化全局最优解 bestsol = empty_bee.deepcopy() bestsol.cost = np.inf # 种群初始化 pop = empty_bee.repeat(npop) for i in range(npop): pop[i].position = np.random.uniform(varmin, varmax, nvar) pop[i].cost = costfunc(pop[i].position) if pop[i].cost < bestsol.cost: bestsol = pop[i].deepcopy() # 初始化每个个体的抛弃次数 count = np.empty(npop) # 记录每一代中全局最优个体目标函数值 bestcost = np.empty(maxit) # 人工蜂群算法主循环 for it in range(maxit): # 引领蜂 for i in range(npop): # 随机选择k，不等于i K = np.append(np.arange(0,i),np.arange(i+1,npop)) k = K[np.random.randint(K.size)] # 定义加速系数 phi = a * np.random.uniform(-1, 1, nvar) # 新的蜜蜂位置 newbee.position = pop[i].position + phi * (pop[i].position - pop[k].position) # 计算新蜜蜂目标函数值 newbee.cost = costfunc(newbee.position) # 通过比较目标函数值，更新第i个蜜蜂的位置 if newbee.cost < pop[i].cost: pop[i] = newbee.deepcopy() else: count[i] += 1 # 计算适应度值和选择概率 fit = np.empty(npop) meancost = np.mean([pop[i].cost for i in range(npop)]) for i in range(npop): fit[i] = np.exp(-pop[i].cost/meancost) #将目标函数值转换为适应度值 probs = fit / np.sum(fit) # 跟随蜂 for m in range(nonlooker): # 通过轮盘赌的方式选择蜜源 i = roulette_wheel_selection(probs) # 随机选择k，不等于i K = np.append(np.arange(0, i), np.arange(i + 1, npop)) k = K[np.random.randint(K.size)] # 定义加速系数 phi = a * np.random.uniform(-1, 1, nvar) # 新的蜜蜂位置 newbee.position = pop[i].position + phi * (pop[i].position - pop[k].position) # 计算新蜜蜂目标函数值 newbee.cost = costfunc(newbee.position) # 通过比较目标函数值，更新第i个蜜蜂的位置 if newbee.cost < pop[i].cost: pop[i] = newbee.deepcopy() else: count[i] += 1 # 侦察蜂 for i in range(npop): if count[i] > limit: pop[i].position = np.random.uniform(varmin, varmax, nvar) pop[i].cost = costfunc(pop[i].position) count[i] = 0 # 更新全局最优解 for i in range(npop): if pop[i].cost < bestsol.cost: bestsol = pop[i].deepcopy() # 存储每一代全局最优解的目标函数值 bestcost[it] = bestsol.cost # 展示迭代信息 print("Iteration {}: Best Cost = {}".format(it, bestcost[it])) # 返回值 out = structure() out.pop = pop out.bestsol = bestsol out.bestcost = bestcost return out def roulette_wheel_selection(p): c = np.cumsum(p) r = sum(p) * np.random.rand() ind = np.argwhere(r <= c) return ind[0][0]