Simulated Annealing

Simulated annealing is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent.

• At the beginning, SA accepts worse and better solutions because the temperature is high;
• At the end of the optimization process, the temperature is very low and SA only accepts better solutions - behaves like hill climbing;
• The probability function of temperature is exponential: $P(\Delta E, T) = e^{-\frac{f(s’) - f(s)}{T}}$.

Implementation

1. Make a random initial solution $s$;
2. Make $T = Tmax$;
3. Select a neighbor $s’$ of $s$;
   1. If $f(s’) < f(s)$, then $s = s’$;
   2. Else, $s = s’$ with probability $e^{-\frac{f(s’) - f(s)}{T}}$;
   3. Repeat step 3 k times;
4. Temperature is updated: $T = T * \alpha$;
5. If $T < Tmin$, then stop; else, go to step 3.

A well known approach to vary temperature is to use a exponentially-decreasing function: $T(t) = Tmax * exp(-R * t)$, where $R$ is a temperature reduction rate (constant), and $Tmax$ is the initial temperature - a large value compared to $R$.

Methods

1. getInitialSolution(): returns a random solution;
2. getNeighbor(solution): returns a random neighbor of the given solution;
3. evaluate(solution): returns the fitness of the given solution;
4. isOptimal(solution): returns true if the given solution is optimal.