simple simulated annealing

This repo contains a very simple implementation of simulated annealing based on the tutorial available [here].

Installation

You can either download/clone this repo or pip install it as follows:

pip install git+https://github.com/nathanrooy/simulated-annealing

Usage: continuous optimization

There are two modes of optimization currently available with this implementation of simulated annealing: continuous and combinatorial. We'll cover the continuous case first but prior to starting we'll need to specify a cost function. For this, we'll install the Landscapes optimization test function library with either of the following commands:

pip install landscapes

pip install git+https://github.com/nathanrooy/landscapes

Now In a Python terminal, import the necessary dependencies. We'll use the sphere function from Landscapes to start off with since it's smooth and convex.

>>> from simulated_annealing import sa
>>> from landscapes.single_objective import sphere

Next, let's specify the initial values and optimize. The value x0 in this case is simply a random starting point with three degrees of freedom.

>>> x0 = [1, 2, 3]
>>> opt = sa.minimize(sphere, x0, opt_mode='continuous', step_max=1000, t_max=1, t_min=0)

The results can be viewed with the following:

>>> opt.results()

Which will yield something fairly close to this:

+------------------------ RESULTS -------------------------+

      opt.mode: continuous
cooling sched.: linear additive cooling


  initial temp: 1
    final temp: 0.001000
     max steps: 1000
    final step: 1000

  final energy: 0.007882

+-------------------------- END ---------------------------+

For additional results, use opt.best_state to view the optimal parameters:

>>> opt.best_state
[0.006638773548345078, -0.08591710990585566, -0.02136864187181653]

As well as opt.best_energy to display the cost function value with these parameters:

>>> opt.best_energy
0.007882441944247037

Usage: combinatorial optimization

For combinatorial problems such as the traveling salesman problem, usage is just as easy. First, let's define a method for calculating the distance between our points. In this case, Euclidean distance is used, but it can be anything...

def calc_euclidean(p1, p2):    
    return ((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)**0.5

Next, let's prepare some points. In the intrest of simplicity, we'll just generate 6 points on the perimiter of the unit circle.

from math import cos
from math import sin
from math import pi
n_pts = 6
d_theta = (2 * pi) / n_pts
theta = [d_theta * i for i in range(0, n_pts)]
x0 = [(cos(r), sin(r)) for r in theta]

Now, prepeare the cost function.

from landscapes.single_objective import tsp
cost_func = tsp(calc_euclidean, close_loop=True).dist

Because we generated our perimiter points in rotational order, x0 is already the optimal solution. We can check this with:

>>> cost_func(x0)
>>> 6.0

Just under two pi...

Now let's optimize this while remembering to shuffle the points prior to running.

from random import shuffle
shuffle(x0)
opt = sa.minimize(cost_func, x0, opt_mode='combinatorial', step_max=1000, t_max=1, t_min=0)

The results should look something like the following:

>>> opt.results()
+------------------------ RESULTS -------------------------+

      opt.mode: combinatorial
cooling sched.: linear additive cooling


  initial temp: 1
    final temp: 0.001000
     max steps: 1000
    final step: 1000

  final energy: 6.000000

+-------------------------- END ---------------------------+

Cooling Schedules

There are several cooling schedules available with this implementation. They are as follows: linear, exponential, logarithmic, and quadratic. They can be specified as using the cooling_schedule= input as follows:

opt = sa.minimize(cost_func, x0, opt_mode='combinatorial', cooling_schedule='linear', ...)