A simple generic simulated annealing solver, and basic implementations of the "standard" acceptance probability function and exponential-decay temperature schedules.

anneal.py

from random import Random
import math

def anneal(initial, energyfn, candidatefn, acceptancefn, temperaturefn, tmax):
    rand = Random()

    t = 0
    solution = initial
    s_best = initial
    e_best = energyfn(initial)

    while (t <= tmax):
        temp = temperaturefn(float(t) / tmax)

        candidate = candidatefn(solution)
        e_cur = energyfn(solution)
        e_cand = energyfn(candidatefn)

        p_accept = acceptancefn(e_cur, e_cand, temp)
        if rand.random() <= p_accept:
            solution = candidate

        if e_cand < e_best:
            s_best = candidate
            e_best = e_cand

        t += 1

    return s_best

def standard_accept(e_cur, e_cand, temp):
    if e_cand <= e_cur:
        return 1
    if temp <= 0:
        return 0
    return math.exp(-(e_cand - e_cur)/temp)

def exp_decay(initial, decay_const=5, zero_threshold=0.95):
    def f(t_frac):
        if t_frac >= zero_threshold:
            return 0.0
        return initial * math.exp(-decay_const * t_frac)
    return f