Minimizing Math Function with Simulated Annealing Algorithm

SimulatedAnnealing.py

import math
import random

def calculateCost(x,y):
    return -0.0001*((
        math.fabs(
            math.sin(x)*math.sin(y)*math.exp(
                math.fabs(
                    100 - math.sqrt(x**2+y**2)/math.pi
                )
            )
        )+1
                    )**0.1)

def random_generator():
    range = 5
    return random.random()*range-range/2

def perform_Simulated_Annealing():
    max_value = 10

    cuurentX=random.randint(0,max_value)
    currentY = random.randint(0,max_value)

    temperature = 100
    iteration = 1
    while 1:
        current_state = calculateCost(cuurentX,currentY)

        tempX = cuurentX +random_generator()
        tempY = currentY+random_generator()
        next_state = calculateCost(tempX,tempY)

        if next_state < current_state:
            currentY = tempY
            cuurentX = tempX
        else:
            probablity =math.exp(-1*(next_state-current_state)/temperature)
            if random.random() <= probablity:
                currentY = tempY
                cuurentX = tempX

        iteration = iteration+1
        temperature = temperature * 0.98

        if iteration > 1000:
            if current_state < next_state:
                next_state = current_state
            return cuurentX,currentY,next_state

answer = perform_Simulated_Annealing()
x,y,ans = answer
print ("values of x,y: ",x,y," minimized value: ",ans)