java實作爬山算法_爬山算法簡介和Python實作執行個體

import random

def evaluate(x1, x2, x3):

return x1+x2-x3

if __name__ == '__main__':

x_range = [ [-2, 5], [2, 6], [-5, 2] ]

best_sol = [random.randint(x_range[0][0], x_range[0][1]),

random.randint(x_range[1][0], x_range[1][1]),

random.randint(x_range[2][0], x_range[2][1])]

while True:

best_evaluate = evaluate(best_sol[0], best_sol[1], best_sol[2])

current_best_value = best_evaluate

sols = [best_sol]

for i in xrange(len(best_sol)):

if best_sol[i] > x_range[i][0]:

sols.append(best_sol[0:i] + [best_sol[i]-1] + best_sol[i+1:])

if best_sol[i] < x_range[i][1]:

sols.append(best_sol[0:i] + [best_sol[i]+1] + best_sol[i+1:])

print sols

for s in sols:

el = evaluate(s[0], s[1], s[2])

if el < best_evaluate:

best_sol = s

best_evaluate = el

if best_evaluate == current_best_value:

break

print 'best sol：', current_best_value, best_sol

某次運作結果如下：

[[0, 5, 1], [-1, 5, 1], [1, 5, 1], [0, 4, 1], [0, 6, 1], [0, 5, 0], [0, 5, 2]]

[[-1, 5, 1], [-2, 5, 1], [0, 5, 1], [-1, 4, 1], [-1, 6, 1], [-1, 5, 0], [-1, 5, 2]]

[[-2, 5, 1], [-1, 5, 1], [-2, 4, 1], [-2, 6, 1], [-2, 5, 0], [-2, 5, 2]]

[[-2, 4, 1], [-1, 4, 1], [-2, 3, 1], [-2, 5, 1], [-2, 4, 0], [-2, 4, 2]]

[[-2, 3, 1], [-1, 3, 1], [-2, 2, 1], [-2, 4, 1], [-2, 3, 0], [-2, 3, 2]]

[[-2, 2, 1], [-1, 2, 1], [-2, 3, 1], [-2, 2, 0], [-2, 2, 2]]

[[-2, 2, 2], [-1, 2, 2], [-2, 3, 2], [-2, 2, 1]]

best sol： -2 [-2, 2, 2]