Simplest simulated annealing algorithm.

sa.py

import numpy as np

class Annealer():

    def __init__(self, step_function, energy_function):
        self.step_function = step_function
        self.energy_function = energy_function

    def run(
        self, state, temperature, room_temperature, cooling_factor
        ):
        best_state = state
        energy = self.energy_function(state)
        energy_curve = [energy] # tracking convergence
        while temperature > room_temperature:
            new_state = self.step_function(state)
            new_energy = self.energy_function(new_state)
            rand = np.random.rand()
            if np.exp(-(energy - new_energy) / temperature) > rand:
                state = new_state
                energy = self.energy_function(state)
                if new_energy > energy_curve[-1]:
                    best_state = new_state
                    energy_curve.append(new_energy)

            temperature *= cooling_factor

        return best_state, energy_curve

Based on info from
http://en.wikipedia.org/wiki/Simulated_annealing
and
http://www.theprojectspot.com/tutorial-post/simulated-annealing-algorithm-for-beginners/6