# Create the weighted graph object used for all tests

graphgen = graphGenerator(10, 20, 100)

randomgraph = graphgen.wgraph

# Greedy Algorithm testing

# Does not require more than one run, as every run yields the same result and approximately the same time

startgreedy = timeit.default_timer()

greedyresult = randomgraph.greedytsp(0)

elapsedgreedy = timeit.default_timer() - startgreedy

print "Final length of Greedy TSP Tour:", greedyresult[1]

# print "Final Greedy TSP Algorithm pathlist:", greedyresult[0]

# Simulated Annealing Algorithm testing

# Run 100 annealing sessions and return basic statistical

# analysis of resulting tour lengths and time

simannealdata = []

simannealtimedata = []

simnumruns = 100

for i in range (simnumruns):

startanneal = timeit.default_timer()

graph = SimAnnealingTSPGraph(randomgraph)

agent = SimAnnealingTSPAgent()

bestgraph = graph

count = 0

shufflecount = 0

while count < 10:

if shufflecount >= 1:

random.shuffle(tspgraph.pathlist)

tspgraph = agent.anneal(graph)

if tspgraph.graph.pathlength(tspgraph.pathlist) < bestgraph.graph.pathlength(bestgraph.pathlist):

bestgraph = tspgraph

count +=1

shufflecount +=1

simelapsed = timeit.default_timer() - startanneal

simannealdata.append(bestgraph.graph.pathlength(bestgraph.pathlist))

simannealtimedata.append(simelapsed)

print "Final length of Simulated Annealing TSP Tour for run",i+1,":", bestgraph.graph.pathlength(bestgraph.pathlist)

# print "Final Simulated Annealing TSP Algorithm path-list for run",i+1,":", bestgraph.pathlist

# Genetic Algorithm testing

# Run 100 instances of the genetic algorithm and return basic

# statistical analysis of resulting tour lengths and time

target_score = greedyresult[1]

geneticdata = []

genetictimedata = []

gennumruns = 100

for j in range (gennumruns):

startgenetic = timeit.default_timer()

genetictsp = evolve(randomgraph, target_score)

print "Final length of Genetic TSP tour for run",j+1,":", genetictsp[1]

# print "Final Genetic TSP tour path-list for run",j+1,":", genetictsp[0].pathlist

geneticelapsed = timeit.default_timer() - startgenetic

geneticdata.append(genetictsp[1])

genetictimedata.append(geneticelapsed)

print "========================================================"

print "================== FINAL RESULTS ======================="

print "========================================================"

print "============= GREEDY ALGORITHM RESULTS ================="

print "Minimum tour length:", greedyresult[1]

print "Time elapsed:", elapsedgreedy, "seconds"

print "========================================================"

print "=========== SIMULATED ANNEALING RESULTS ================"

print "Out of", simnumruns, "runs:"

print "Minimum (best) value:", min(simannealdata)

print "Mean value:", float(sum(simannealdata))/len(simannealdata)

print "Range:", min(simannealdata), "to", max(simannealdata)

print "Minimum (best) time:", min(simannealtimedata), "seconds"

print "Mean time:", float(sum(simannealtimedata))/len(simannealtimedata), "seconds"

print "Range of times:", min(simannealtimedata), "to", max(simannealtimedata), "seconds"

print "========================================================"

print "============== GENETIC ALGORITHM RESULTS ==============="

print "Out of", gennumruns, "runs:"

print "Minimum (best) value:", min(geneticdata)

print "Mean value:", float(sum(geneticdata))/len(geneticdata)

print "Range:", min(geneticdata), "to", max(geneticdata)

print "Minimum (best) time:", min(genetictimedata), "seconds"

print "Mean time:", float(sum(genetictimedata))/len(genetictimedata), "seconds"

print "Range of times:", min(genetictimedata), "to", max(genetictimedata), "seconds"