# op.printschedule(s)
# print(op.schedulecost(s))
domain = [(0, 9)] * (len(op.people) * 2)
# s = op.randomoptimize(domain, op.schedulecost)
# s = op.hillclimb(domain, op.schedulecost)
# s = op.annealingoptimize(domain, op.schedulecost)
# s = op.geneticoptimize(domain, op.schedulecost)
# print(domain,s)
# print(op.schedulecost(s))
# op.printschedule(s)
result = {'rand': [], 'hill': [], 'anne': [], 'gene': []}
for i in range(10):
s1 = op.randomoptimize(domain, op.schedulecost)
result['rand'].append(op.schedulecost(s1))
s2 = op.hillclimb(domain, op.schedulecost)
result['hill'].append(op.schedulecost(s2))
s3 = op.annealingoptimize(domain, op.schedulecost)
result['anne'].append(op.schedulecost(s3))
s4 = op.geneticoptimize(domain, op.schedulecost)
result['gene'].append(op.schedulecost(s4))
print(result)
print('rand', sum(result['rand']) / 10, min(result['rand']))
print('hill', sum(result['hill']) / 10, min(result['hill']))
print('anne', sum(result['anne']) / 10, min(result['anne']))
print('gene', sum(result['gene']) / 10, min(result['gene']))
import optimization reload(optimization) domain=[(0,9)]*(len(optimization.people)*2) s=optimization.hillclimb(domain,optimization.schedulecost) print optimization.schedulecost(s)
import time
import random
import math
import optimization
def randomoptimize(domain, costf):
best = 999999999
bestr = None
for i in range(1000):
# 無作為解の生成
r = [random.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
# コストの取得
cost = costf(r)
# 最良解と比較
if cost < best:
best = cost
bestr = r
return bestr # 書籍にはrと書かれていた
if __name__ == '__main__':
domain = [(0,9)]*(len(optimization.people)*2) # 書籍には(0.8)と書かれていた
s = randomoptimize(domain, optimization.schedulecost)
optimization.printschedule(s)
cost = optimization.schedulecost(s)
print('cost is %4s' % (cost))
def test_cal_schedule_cost():
s = [1, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3]
print optimization.schedulecost(s)
import random
import optimization
# search for best solution by creating random
# solutions and checking their cost.
def randomsearch(domain, costf):
best = 999999999
bestsol = None
# random number of iterations.
for i in range(1000):
# create a random solution.
sol = [
random.randint(domain[i][0], domain[i][1])
for i in range(len(domain))
]
# calculate the cost.
cost = costf(sol)
if best > cost:
best = cost
bestsol = sol
return sol
# for each person there are 9 outbound and inbound flights.
domain = [(0, 8)] * (len(optimization.dataset) * 2)
sol = randomsearch(domain, optimization.schedulecost)
print(optimization.schedulecost(sol))
print(u'1. Исходные данные:')
print('----------------------------------')
optimization.printpeople()
print('')
print(u'Аэропорт встречи: ')
print(optimization.destination)
print('')
print('')
print(u'2. Оптимизация:')
print('----------------------------------')
domain = [(0,8)]*(len(optimization.people)*2)
print(u'2.1 Алгоритм случайного поиска...')
srand = optimization.randomopt(domain, optimization.schedulecost);
rand = optimization.schedulecost(srand)
print('')
print(u'2.2 Алгоритм спуска с горы...')
shill = optimization.hilldownopt(domain, optimization.schedulecost);
hill = optimization.schedulecost(shill)
print('')
print(u'2.3 Алгоритм имитации отжига...')
sanneal = optimization.annealingopt(domain, optimization.schedulecost);
anneal = optimization.schedulecost(sanneal)
print('')
print(u'2.4 Генетический алгоритм...')
sgen = optimization.geneticopt(domain, optimization.schedulecost);
gen = optimization.schedulecost(sgen)
def test_find_optimized_schedule(optimization_method):
domain = [(0, 8)] * (len(optimization.people) * 2)
r = optimization_method(domain, optimization.schedulecost)
optimization.printschedule(r)
print optimization.schedulecost(r)
#coding:utf-8 import optimization #s = [1,4,3,2,7,3,6,3,2,4,5,3] #print optimization.printschedule(s) #print optimization.schedulecost(s) domain = [(0, 9)] * len(optimization.people) * 2 #s = optimization.randomoptimize(domain,optimization.schedulecost) #s = optimization.hillclimb(domain,optimization.schedulecost) #s = optimization.annealingoptimize(domain,optimization.schedulecost) s = optimization.geneticoptimize(domain, optimization.schedulecost) print optimization.schedulecost(s) print optimization.printschedule(s) ''' 这两种及大多数优化方法都假设：大多数问题，最优解应该接近于其他的最优解。 但某些特殊情况不一定有效。比如存在陡峭的突变的最优解。 '''
import optimization
import dorm
import socialnetwork
# Group Travel Problem
print('<----Group Travel---->')
s = [1, 4, 3, 2, 7, 3, 6, 3, 2, 4, 5, 3]
optimization.printschedule(s)
cost = optimization.schedulecost(s)
print('Present cost:%f' % cost)
print('\n<----Random Searching---->')
domain = [(0, 9)] * (len(optimization.people) * 2)
print(s)
s = optimization.randomoptimize(domain, optimization.schedulecost)
cost = optimization.schedulecost(s)
print('Present cost:%f' % cost)
optimization.printschedule(s)
print('\n<----Hill Climbing---->')
s = optimization.hillclimb(domain, optimization.schedulecost)
print(s)
cost = optimization.schedulecost(s)
print('Present cost:%f' % cost)
optimization.printschedule(s)
print('\n<----Simulated Annealing---->')
s = optimization.annealingoptimize(domain, optimization.schedulecost)
print(s)
cost = optimization.schedulecost(s)
from pprint import pprint import optimization domain = [(0, 9)] * (len(optimization.people) * 2) # random search s = optimization.randomoptimize(domain, optimization.schedulecost) print(optimization.schedulecost(s)) pprint(optimization.printschedule(s)) # hillclimb s = optimization.hillclimb(domain, optimization.schedulecost) print(optimization.schedulecost(s)) optimization.printschedule(s) # simulated annealing s = optimization.annealingoptimize(domain, optimization.schedulecost) print(optimization.schedulecost(s)) pprint(optimization.printschedule(s)) # genetic algorithms s = optimization.geneticoptimize(domain, optimization.schedulecost) # print(optimization.schedulecost(s)) pprint(optimization.printschedule(s))
import optimization as op import time s = [1,4,3,2,7,3,6,3,2,4,5,3] domain = [(0,9)]*len(s) print 'fix:', start = time.clock() print op.schedulecost(s) op.printschedule(s) print 'running time',time.clock()-start print print 'random optimization:', start = time.clock() sol = op.randomoptimize(domain) print op.schedulecost(sol) op.printschedule(sol) print 'running time',time.clock()-start print print 'hill climb:', start = time.clock() sol = op.hillclimb(domain) print op.schedulecost(sol) op.printschedule(sol) print 'running time',time.clock()-start print print 'annealing optimization:', start = time.clock()
# for i in range(100):
# # domain: the range of each value in the solution, the range is decided on how many choices of each pair of two places can be choosed
# domain=[(0,3)]*(len(optimization.people)*2)
# # run the optimization
# s=optimization.annealingoptimize(domain,optimization.schedulecost)
# # print the cost of final solution
# print('The schedule cost is ',optimization.schedulecost(s))
# # print(optimization.schedulecost(s))
# print()
# # print the final solution
# optimization.printschedule(s)
# domain: the range of each value in the solution, the range is decided on how many choices of each pair of two places can be choosed
domain = [(0, 3)] * (len(optimization.people) * 2)
# run the optimization
s = optimization.annealingoptimize(domain, optimization.schedulecost)
# print the cost of final solution
print('The schedule cost is ', optimization.schedulecost(s))
# print(optimization.schedulecost(s))
print()
# print the final solution
optimization.printschedule(s)
def test_schedulecost(s):
sys.stderr.write("testing schedulecost...\n")
print optimization.schedulecost(s)
sys.stderr.write("??? expecting 5285 according to textbook???...\n")
def test_annealing(domain):
sys.stderr.write("testing annealing optimization...\n")
s = optimization.annealingoptimize(domain, optimization.schedulecost)
print optimization.schedulecost(s)
optimization.printschedule(s)
def test_hillclimb(domain):
sys.stderr.write("testing hillclimb optimization...\n")
s = optimization.hillclimb(domain, optimization.schedulecost)
print optimization.schedulecost(s)
optimization.printschedule(s)
def test_randomoptimize(domain):
sys.stderr.write("testing random optimization...\n")
s = optimization.randomoptimize(domain, optimization.schedulecost)
print optimization.schedulecost(s)
optimization.printschedule(s)
