I’m currently studying a whole bunch of nature inspired algorithms, or in other words optimisation methods inspired by natural phenomena.

The simulated annealing (SA) algorithm is an optimisation method that is able to also solve complex optimisation problems. The strength of SA is its ability to overcome the ‘neighbourhood limit’ that restricts simple optimisation algorithms.

A classical example of the applicability of SA, also emphasised in the original article Optimization by Simulated Annealing (Kirkpatrick et al. 1983), is its ability to solve the traveling salesman problem (a well known NP problem where we want to optimise a travel person’s path, so (s)he travels the least amount of miles).

## Optimisation algorithms in general

In large terms, most optimisation algorithms will generate a random solution and explore the nearby area of solutions to see if it yields better solutions than the current one. For simple algorithms, if a neighbouring solution is better than the current one, the algorithm will settle on this instead. If not, the algorithm has probably found the best solution.

The ‘size of the neighbourhood’ therefore becomes the limit of efficiency, and this can be problematic for some functions. Say, we want to find the global maximum of the following graph, and that we initiate our simple algorithm at the star. We will find better solutions towards Top 5, until we reach this solution and (wrongly) conclude that there are no better solutions in the neighbourhood.

## The nature of simulated annealing

SA is based on an analogy with the annealing of metals. Strong metals are created by the heating and step-wise cooling of the material. In the beginning, when the material is completely melted, there is no order or structure in the atoms. As they flow about, the material changes state (configuration of atoms) constantly. During each cooling step, the material is given some time to allow it to relax and slowly harden into a stable structure. If you cool it too fast, some areas will settle in random non-ordered structures from the melting phase, and you will end up with a weaker metal.

Extending this process to the problem of function optimisation has shown surprising potential, and the analogy is so rich that the extension with thermodynamic analogies guides and explains optimisation approaches surprisingly well.

## The big picture

Let’s bring this analogy back down to Earth for a moment; we will jump anywhere in the domain of whatever function we are trying to optimise and slowly narrow in on the optimum using various criteria to ensure this. A gross simplification of the annealing schedule is illustrated below, but it captures the essence of SA. Already in the first jump (the blue one), we cross two minima we wouldn’t have crossed using a simple algorithm. This is what helps the algorithm to determine decent approximations to complex optimisation problems.

## Pseudocode steps

Let’s continue to work with a simplification of the algorithm, and gradually add more to it. The essential steps of the algorithm is to:

1. Identify an objective and cost function
2. Initiate a random solution and calculate its cost
3. Generate a random neighbouring solution and calculate its cost too
4. Accept the new solution with some probability
• Let the solution settle

#### Identify an objective and cost function

This is entirely up to you – your objective function is whatever function you want to optimise, and the cost function is whatever ‘price’ this solution represents. Notice that you have to calculate the cost of every proposed solution, so keep it as simple as possible in order to keep your algorithm efficient. It could simply be the function value (the y-value to the proposed x-value) or the number of miles traveled by the traveling sales person, and sometimes it has to be more complex. It entirely depends on your optimisation problem.

#### Initiate a random solution and calculate its cost

You don’t have to guess anything near the optimal solution, just use a random generator to initiate the algorithm and calculate its cost.

#### Generate a random neighbouring solution and calculate its cost too

To generate a ‘random neighbouring’ solution, all you have to do is switch two random elements of your current solution. The main requirement is that the two elements are randomly chosen. Then, calculate its cost too.

#### Accept the new solution with some probability

Random movement in optimisation yields surprising benefits – that’s why we generate random neighbourhood solutions. But sometimes, we need to cross through a ‘bad’ neighbourhood to get closer to the best solution – especially if we’re far away from it to begin with. With this in mind, we need to define an acceptance probability that will always move towards better solutions, but will also sometimes go towards worse solutions. To help keep the cost function simple, we need to exaggerate the difference between slightly better solutions (very close neighbours have almost the same cost), so we want to use the exponential difference of the costs:

$$P_{acc} = \exp {\text{cost}_{\text{proposed}} – \text{cost}_{\text{current}} }$$

As described in the beginning, the analogy is centred around the cooling schedule of a system which allows it to slowly settle into an optimal solution, so we introduce a temperature parameter, $$T$$. Usually, we start the temperature at 1 and slowly decrease it using an $$\alpha$$-value typically between 0.8 and 0.99. The temperature is therefore dependent on the number of iterations you have gone through, and we want it to affect how much we will accept random movements away from the current optimal solution. After N iterations, we’d prefer the algorithm to confidently settle on a solution and not accidentally end up at an entirely random solution. We therefore include it in the acceptance probability (this also has strong connection with the actual thermodynamic formulation):

$$P_{acc} = \exp {\frac{\text{cost}_{\text{proposed}} – \text{cost}_{\text{current}}}{T} }$$

Using random uniform numbers between 0 and 1, we will accept a proposed solution as our next move if the acceptance probability is larger than the random number. After N iterations, it will thus be highly unlikely to move towards worse solutions, and all we’d have to do to see that we hit a rare coincidence would be to run the algorithm a few times.

##### Letting the optimal solution settle in each step

For each iteration, that is for each value of T, we should run the algorithm (with this T-value), somewhere between 100-1000 times. This is analogous to the step-wise cooling scheme of actual annealing where the material gets time to settle. Remember, as the temperature decreases, so does the probability of random movement, but we still need to explore the possible configurations at that temperature before continuing, otherwise we might end up in a random place and suddenly just get stuck because the temperature decreases too fast. With gradual exploration at each temperature step, we’re less likely to end up at the wrong optimal solution.

## Pseudo code

To really pull SA down to Earth, take a look at the following (incomplete) script. It is only meant to highlight the characteristics of the algorithm. I plan on describing my implementation of SA in a graph partitioning example, if I get the time. If I do, I’ll make sure to include a link for it in this post.


from random import random as r

def annealing_schedule(some_solution):
current_optimal_solution = some_solution
cost_old = cost(current_optimal_solution)
T = 1
T_min = 0.000001
alpha = 0.99

while T &gt; T_min:
i = 1
while i &lt;= 500:
proposed_solution = neighbour(current_solution)
cost_proposed = cost(proposed_solution)
P_acc = acceptance_probability(cost_proposed, cost_old, T)
if P_acc &gt; r():
current_optimal_solution = proposed_solution
cost_old = cost_proposed
i += 1
T *= alpha

return current_optimal_solution



## Final remarks

There are many variations of simulated annealing, but this article stays true to the core principles without drawing too much on the original articles which I found difficult to follow and translate into generalisable programming. Simulated annealing essentially is kind of a mix of a hill climbing method and an extended Metropolis algorithm. It gradually chooses the better solutions, but jumps randomly in the neighbourhood and will sometimes choose poorer solutions similar to the Metropolis algorithm.

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