the suboprocesses are running and the algorithm is functionning to optimize the function it is given

SourceCode/AlgorithmAnalyser.py

@@ -0,0 +1,40 @@
+# directly running the DOE because existing surrogates can be explored with another workflow
+
+from numpy import random as r
+import time
+import importlib.util
+import multiprocessing
+
+
+# initialise the logic helpers
+r.seed(int(time.time()))
+
+heuristicpath = "/home/remi/Documents/MDAF-GitLAB/SourceCode/SampleAlgorithms/SimmulatedAnnealing.py"
+heuristic_name = "SimmulatedAnnealing"
+testfunctionpaths = ["/home/remi/Documents/MDAF-GitLAB/SourceCode/TestFunctions/Bukin2.py"]
+funcnames = ["Bukin2"]
+objs = 0
+args = {"high": 200, "low": -200, "t": 0.01, "p": 0.8}
+scale = 62
+
+
+def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale):
+    spec = importlib.util.spec_from_file_location(heuristic_name, heuristicpath)
+    heuristic = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(heuristic)
+    proc = list()
+    #heuristic.MyClass()
+    for idx, funcpath in enumerate(testfunctionpaths):
+        testspec = importlib.util.spec_from_file_location(funcnames[idx], funcpath)
+        func = importlib.util.module_from_spec(testspec)
+        testspec.loader.exec_module(func)
+        #func.MyClass()
+        initpoint = [r.random() * scale, r.random() * scale]
+        proc.append(multiprocessing.Process(target=heuristic.main, name=funcnames[idx], args=(func, objs, initpoint, args)))
+        best = proc[idx].run()
+        print("started :" + str(initpoint) + "\nEnded  :" + str(best))
+
+# simulatedAnnealing(S = [9.00,4.00],y = 0,high = 10,low = -8,t =0.01,p = 0.8)
+# proc = subprocess.call(["python", heuristic, "arg-15", "arg2", "argN"])
+
+doe (heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args, scale)