基于布谷鸟算法的基站选址实例.docx

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1、实例描述假设某个区域中有IOO个候选基站位置，需要从中选择20个位置建设基站，以最小化覆盖所有用户的成本。假设共有500个用户需要覆盖，用户分布在整个区域内。代码实现importrandomimportmathc1assCuckooSearch:def_init_(se1f,n,m,k,func,1b,ub,pa=0.25,a1pha=1.0):se1f.n=n#种群数量se1f.m=m#基站数量se1f.k=k#用户数量se1f.func=func#适应度函数se1f.1b=1b#搜索空间下界se1f.ub=ub#搜索空间上界se1f.pa=pa#概率参数se1f.a1pha=a1pha#步

2、长参数se1f.popu1ation=#种群列表se1f.best_so1ution=None#最优解se1f.best_fitness=f1oat(,inf)#最优解的适应度值se1f.init_popu1ation()definit_popu1ation(se1f):foriinrange(se1f.n):so1ution=random.uniform(se1f.1b,se1f.ub)forjinrange(se1f.m)fitness=se1f.func(so1ution)se1f.popu1ation.append(so1ution,fitness)iffitnessse1f.best

3、_fitness:se1f.best_so1ution=so1utionse1f.best_fitness=fitnessdefrun(setgenerations):forginrange(generations):foriinrange(se1f.n):#随机选择另一个鸟的位置j=random.randint(0,se1f.n-1)whi1ej=i:j=random.randint(0zse1f.n-1)# 生成新的解new_so1ution=se1f.get_new_so1ution(se1f.popu1ationi0zse1f.popu1ationj0)# 计算新解的适应度值new_f

4、itness=se1f.func(new_so1ution)# 判断是否替换原种群中的个体ifnew_fitnessse1f.popu1ationi1:se1f.popu1ationi=(new-so1utionznew_fitness)# 判断是否替换当前的最优解ifnew_fitnessse1f.best_fitness:se1f.best_so1ution=new_so1utionse1f.best_fitness=new_fitness#进行种群更新se1f.popu1ation=se1f.update_popu1ation()defget_new_so1ution(se1t,xj):

5、new_so1ution=xi.copy()foriinrange(se1f.m):#生成新的位置u=random.uniform(0,1)v=random.uniform(0,1)step=se1f.a1pha*(xii-xji)ifuse1f.pa:new_so1utioni=ii+step*ve1se:new_so1utioni=ii#判断是否越界ifnew_so1utionise1f.ub:new_so1utioni=se1f.ubreturnnew_so1utiondefupdate_popu1ation(se1f):#对种群进行排序se1f.popu1ation.sort(key=

6、1ambdax:x1)#生成新的种群new_popu1ation=foriinrange(se1f.n):#生成新的鸟巢new_so1ution=se1f.get_new_so1ution(se1f.popu1ationi0,se1f.best_so1ution)new_fitness=se1f.func(new_so1ution)new-popu1ation.append(new-so1utionznew-fitness)returnnew_popu1ationdefcoverage_cost(so1ution):# 计算覆盖成本cost=0foriinrange(100):min_dist

7、ance=f1oat(,inf)forjinrange(20):distance=math.sqrt(so1utionj-i)*2)ifdistancemin_distance:mindistance=distancecost+=min_distance# 计算基站建设成本bui1d_cost=sum(so1ution)# 计算总成本tota1_cost=cost+bui1d_costreturntota1_costcs=CuckooSearch(n=20,m=20,k=100,func=coverage_cost,Ib=O,ub=1,pa=0.25,a1pha=1.0)cs.run(generations=1000)Print(,最优解cs.beSjSo1UtiOn)Print(,最优解的适应度值cs.best_fitness)