Somewhat sensible default parameters, temperature, amount of maximum anneal iterations and generator accept probability passed to anneal.

src/Generator/generator.py

@@ -154,13 +154,14 @@ def correct_precedence(solution, idx):
             task = (new_start,) + task[1:]
             solution.insert(idx, task)
 
-def generate(old_solution, steps, percent=1):
+def generate(old_solution, steps, p=0.01):
     """
     Generate a new solution from an existing solution with a 
     specified number of max steps.
     """
     import sys
     print("Max steps: " + str(steps))
+    print("Accept probability: " + str(p))
     sys.stdout.write("Start generation... ")
     solution = old_solution[:]
     option = pull_fwd #do at least one pull
@@ -168,13 +169,13 @@ def generate(old_solution, steps, percent=1):
         solution = option(solution)
         if(option == accept):
             break
-        select = random.randrange(0,1000)
-        option = pull_fwd #if (select - percent) > 0 else accept
+        option = pull_fwd if p < random.random() else accept
         if ((i * 100) % steps == 0):
             sys.stdout.write(str(i*100/steps) + "%... ")
             sys.stdout.flush()
-    sys.stdout.write("\n")
-    accept(solution)
+    sys.stdout.write("Done\n")
+    if option != accept:
+        accept(solution)
     return solution
 
 def mock():

src/SchedulingAlgorithms/simanneal.py

@@ -4,15 +4,15 @@ from SchedulingAlgorithms.enumerate import enumerate as enum
 from math import e
 from random import random
 
-def anneal(max_temp = 1000, max_steps = 1000):
+def anneal(max_temp = 300, max_steps = 250, accept_prob=0.01):
     global problem
     gen_init(problem)
     temp = max_temp
     initial = enum(problem)
-    current = generate(initial, problem.machines * problem.jobs * 10)
+    current = generate(initial, problem.machines * problem.jobs * 10, 0) #Complete the iteration once fully.
     del initial
     for step in range(0, max_steps):
-        new = generate(current, problem.machines * problem.jobs)
+        new = generate(current, problem.machines * problem.jobs, accept_prob)
         new_end = rate(new)
         curr_end = rate(current)
         p = 1 / ( 1 + (e ** ((curr_end - new_end)/temp)))