README.md

SimulatedAnnealing

An optimization algorithm modeled on temperature physics! (Thanks to Katrina Geltman!)

###Info Simulated Annealing is a way to generate a 'pretty good' solution to NP problems, such as the knapsack problem or traveling salesman. Its primary advantage over other methods such as hill climbers and genetic algorithms is that a simulated annealing algorithm is relatively less likely to get stuck at local maxima. It does this by sometimes allowing moves to solutions that are actually worse than the current solutions.

The likelihood of this algorithm accepting a bad solution is high at the beginning and decreases over time—this is where the comparison to annealing comes in. Think of excited atoms bouncing around as in a gas. They can spread out and explore a lot of the solution space. But as their internal energy decreases, these atoms congeal around a good (if not best) solution. This analogy gives the simulated annealing algorithm its name.

from wiki

"Simulated annealing searching for a maximum. The objective here is to get to the highest point, however, it is not enough to use a simple hill climb algorithm, as there are many local maxima. By cooling the temperature slowly the global maximum is found."

###Steps

1. First, generate a random solution
2. Calculate its cost using some cost function you've defined
3. Generate a random neighboring solution
4. Calculate the new solution's cost
5. Compare them: If cnew < cold: move to the new solution; If cnew > cold: maybe* move to the new solution
6. Repeat steps 3-5 above until an acceptable solution is found or you reach some maximum number of iterations.

*

###Pseudocode

def anneal(solution):
    old_cost = cost(solution)
    T = 1.0
    T_min = 0.00001
    alpha = 0.9
    while T > T_min:
        i = 1
        while i <= 100:
            new_solution = neighbor(solution)
            new_cost = cost(new_solution)
            ap = acceptance_probability(old_cost, new_cost, T)
            if ap > random(0,1)
                solution = new_solution
                old_cost = new_cost
            i += 1
        T = T*alpha
    return solution