simulated annealing

src/Generator/generator.py

@@ -159,16 +159,21 @@ def generate(old_solution, steps, percent=1):
     Generate a new solution from an existing solution with a 
     specified number of max steps.
     """
+    import sys
+    print("Max steps: " + str(steps))
+    sys.stdout.write("Start generation... ")
     solution = old_solution[:]
-    percent = 1
     option = pull_fwd #do at least one pull
-    while(steps > 0):
+    for i in range(0, steps):
         solution = option(solution)
         if(option == accept):
             break
-        steps -= 1
+        select = random.randrange(0,1000)
-        select = random.randrange(0,100)
+        option = pull_fwd #if (select - percent) > 0 else accept
-        option = pull_fwd if (select - percent) > 0 else accept
+        if ((i * 100) % steps == 0):
+            sys.stdout.write(str(i*100/steps) + "%... ")
+            sys.stdout.flush()
+    sys.stdout.write("\n")
     accept(solution)
     return solution
 

src/SchedulingAlgorithms/simanneal.py

@@ -0,0 +1,36 @@
+from Generator.generator import generate
+from Generator.generator import init as gen_init
+from SchedulingAlgorithms.enumerate import enumerate as enum
+from math import e
+from random import random
+
+def anneal(max_temp = 1000, max_steps = 1000):
+    global problem
+    gen_init(problem)
+    temp = max_temp
+    initial = enum(problem)
+    current = generate(initial, problem.machines * problem.jobs * 10)
+    del initial
+    for step in range(0, max_steps):
+        new = generate(current, problem.machines * problem.jobs)
+        new_end = rate(new)
+        curr_end = rate(current)
+        p = 1 / ( 1 + (e ** ((curr_end - new_end)/temp)))
+        if (new_end < curr_end) or (p < random()):
+            current = new
+            print("Old: " + str(curr_end) + " New: " + str(new_end))
+        temp = ((max_temp-1)/(max_steps**2))*(step-max_steps)**2+1
+        print("Iteration: "+ str(step) + " Temperature: " + str(temp))
+    return current
+
+def rate(solution):
+    global problem
+    last_tasks = []
+    for i in range(0,problem.jobs):
+        last_tasks += [next(( x for x in solution[::-1] if x[1][0] == i), [])]
+    end_times = [ problem[x[1]][0] + x[0] for x in last_tasks]
+    return max(end_times)
+
+def init(in_problem):
+    global problem
+    problem = in_problem