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| from functools import reduce
import matplotlib.pyplot as plt
import numpy as np
def init(pop_size,leng):
population = []
for i in range(pop_size):
pop = ''
for j in range(leng):
pop = pop + str(np.random.randint(0,2))
population.append(pop)
return population
def compute_fitness(population,weight,profit):
weight_list = []
profit_list = []
for pop in population:
tmp_w = 0
tmp_p = 0
for idx in range(len(pop)):
if pop[idx] == '1':
tmp_w += weight[idx]
tmp_p += profit[idx]
weight_list.append(tmp_w)
profit_list.append(tmp_p)
return weight_list,profit_list
def select(weight_limit,population,weight,profit):
pop,w,p = [],[],[]
for idx in range(len(weight)):
if weight[idx] <= weight_limit:
w.append(weight[idx])
p.append(profit[idx])
pop.append(population[idx])
return pop,w,p
def roulette(pop_size,population,total_profit):
sum_profit = reduce(lambda i,j:i+j,total_profit)
p = list(map(lambda i:i/sum_profit,total_profit))
new_population = []
while len(new_population) < pop_size:
tmp = np.random.choice(a=population,size=1,replace=True,p=p)
tmp = tmp.tolist()[0]
new_population.append(tmp)
return new_population
def ga_cross(new_population,pcross):
children = []
leng = len(new_population[0])
while len(children) < len(new_population):
if np.random.uniform() < pcross:
mo_idx = np.random.randint(0,leng)
fa_idx = np.random.randint(0,leng)
mo = new_population[mo_idx]
fa = new_population[fa_idx]
if fa_idx != mo_idx:
seg_idx = np.random.randint(0,leng)
mo_left = mo[0:seg_idx]
mo_right = mo[seg_idx:leng]
fa_left = fa[0:seg_idx]
fa_right = fa[seg_idx:leng]
child1 = mo_left + fa_right
child2 = fa_left + mo_right
children.append(child1)
children.append(child2)
return children
def mutation(population,pmutation):
new_pop = []
leng = len(population[0])
for pop in population:
if np.random.uniform() < pmutation:
idx = np.random.randint(0,leng)
pop = list(pop)
if pop[idx] == '0':
pop[idx] = '1'
else:
pop[idx] = '0'
pop = ''.join(pop)
new_pop.append(pop)
return new_pop
if __name__ == '__main__':
pm = 0.2
pc = 0.8
iters = 600
pop_size = 50
n = 14
weight=[5,7,9,8,4,3,10,14,13,9,6,8,5,15]
profit=[10,8,15,9,6,5,20,10,13,10,7,12,5,18]
weight_limit = 75
population = init(pop_size,n)
cur_iter = 0
ans_pop,ans_w,ans_p = [],[],[]
while cur_iter < iters:
w,p = compute_fitness(population,weight,profit)
s_pop,s_w,s_p = select(weight_limit,population,w,p)
new_pop = roulette(pop_size,s_pop,s_p)
new_pop1 = ga_cross(new_pop,pc)
population = mutation(new_pop1,pm)
cur_iter += 1
w,p = compute_fitness(population,weight,profit)
s_pop,s_w,s_p = select(weight_limit,population,w,p)
idx = s_pop.index(max(s_pop))
if len(ans_p)==0 or s_p[idx] > ans_p[-1]:
ans_pop.append(s_pop[idx])
ans_p.append(s_p[idx])
ans_w.append(s_w[idx])
idx = population.index(max(population))
print("w_limit:",weight_limit)
print("w的变化",ans_w)
print("p的变化",ans_p)
plt.rcParams['font.sans-serif']=['simsun']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(range(len(ans_p)),ans_p,label='装入物品总价值', color='red')
for idx,(w,p) in enumerate(zip(ans_w, ans_p)):
plt.text(idx, p, f'(w:{w},p:{p})')
plt.legend()
plt.show()
|
