模拟退火法求解非线性规划的解

import random

import math



XMAX = 4

YMAX = 4

Tolerance = 0.000001



MarkovLength = 10000

DecayScale = 0.95

StepFactor = 0.02

Temperature = 100

AcceptPoints = 0.0



PreX = -XMAX * random.random()

PreY = - YMAX * random.random()

BestX = PreX;

PreBestX = BestX

BestY = PreY

PreBestY = BestY



Temperature = DecayScale * Temperature



times = 1



def obj_function(x, y):

    return 5.0 * math.sin(x * y) + x * x + y * y



j = 1



while(times < 2 or (math.fabs(obj_function(BestX, BestY) - obj_function(PreBestX, PreBestY)) > Tolerance)):

    j += 1

    print j

    for i in range(MarkovLength):

        NextX = PreX + StepFactor * XMAX * (random.random() - 0.5)

        NextY = PreY + StepFactor * YMAX * (random.random() - 0.5)

        

        while not (NextX >= -XMAX and NextX <= XMAX and NextY >= -YMAX and NextY <= YMAX):

            NextX = PreX + StepFactor * XMAX * (random.random() - 0.5)

            NextY = PreY + StepFactor * YMAX * (random.random() - 0.5)        

        

        if obj_function(BestX, BestY) > obj_function(NextX, NextY):

            PreBestX = BestX

            PreBestY = BestY

            

            BestX = NextX

            BestY = NextY

            

        if obj_function(PreX, PreY) - obj_function(NextX, NextY) > 0:

            PreX = NextX

            PreY = NextY

            AcceptPoints += 1

            

        else:

            change = -1 * (obj_function(NextX, NextY) - obj_function(PreX, PreY)) / Temperature

            if math.exp(change) > random.random():

                PreX = NextX

                PreY = NextY

                AcceptPoints += 1

                

    times += 1





print BestX, BestY, obj_function(BestX, BestY)