simulated annealing

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