import math, random

def distance(city1, city2):
    return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)

def total_distance(cities):
    return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])

class SimulatedAnnealing:
    def __init__(self, cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001):
        self.cities = cities
        self.temperature = temperature
        self.cooling_rate = cooling_rate
        self.temperature_ok = temperature_ok

    def run(self):
        current_solution = self.cities.copy()
        best_solution = self.cities.copy()
        while self.temperature > self.temperature_ok:
            new_solution = current_solution.copy()
            # Swap two cities in the route
            i = random.randint(0, len(new_solution) - 1)
            j = random.randint(0, len(new_solution) - 1)
            new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
            # Calculate the acceptance probability
            current_energy = total_distance(current_solution)
            new_energy = total_distance(new_solution)
            delta = new_energy - current_energy
            if delta < 0 or random.random() < math.exp(-delta / self.temperature):
                current_solution = new_solution
                if total_distance(current_solution) < total_distance(best_solution):
                    best_solution = current_solution
            # Cool down
            self.temperature *= self.cooling_rate
        return best_solution