def firefly_paper(constParams, tuneParams):
evalFun = constParams[0]
evalConstParams = constParams[1]
paramRanges = constParams[2]
stopCond = constParams[3]
stopVal = constParams[4]
repetitions = constParams[5]
verbose = constParams[6]
numFlies = tuneParams[0]
alpha = tuneParams[1]
beta0 = tuneParams[2]
gamma = tuneParams[3]
numDim = len(paramRanges)
posns = [util.uniform_random(paramRanges) for i in range(numFlies)]
bestSol = None
bestSolFitness = sys.float_info.max
bestSolReps = None
stopLoop = False
numEvals = 0
iterations = 0
while not stopLoop:
#Note that, under this implementation, the best solution is always one iteration behind the movement
bestSol, bestSolFitness, bestSolReps, fitness, fitnessReps, newEvals = evaluate_sols(
evalFun, evalConstParams, posns, repetitions)
numEvals += newEvals
posns = firefly_move_paper(posns, fitness, alpha, beta0, gamma,
paramRanges)
iterations += 1
if stopCond == "fitness":
if bestSolFitness < stopVal[0]:
stopLoop = True
elif stopCond == "fitnessOrEvals":
if bestSolFitness < stopVal[0] or numEvals > stopVal[1]:
stopLoop = True
elif stopCond == "fitnessOrGlobalEvals":
if bestSolFitness < stopVal[
0] or test_funs.numEvaluations > stopVal[1]:
stopLoop = True
elif stopCond == "generations":
if iterations >= stopVal[0]:
stopLoop = True
else:
print "ERROR: Stop condition not recognized"
break
if verbose:
print "Iteration: " + str(iterations)
print "Evaluations: " + str(numEvals)
print "Best Solution: " + str(bestSol)
print "Best Mean Fitness: " + str(bestSolFitness)
print "Best Fitness Set: " + str(bestSolReps) + "\n"
return (numEvals, bestSol, bestSolFitness, bestSolReps)
def empty_nests_paper(nests, pa, bestNest, paramRanges, fmin, fminReps, evalFun, evalConstParams, oldFitness, oldFitnessReps, repetitions):
numNests = len(nests)
numDim = len(nests[0])
#sort nests by fitness
sortList = [[nests[i], oldFitness[i], oldFitnessReps[i]] for i in range(numNests)]
sortList.sort(key=lambda x: x[1])
#determine the cutoff past which nests will be replaced
breakpoint = int(round((1-pa)*numNests))
if breakpoint == numNests:
breakpoint -= 1
returnNests = []
returnFitness = []
returnReps = []
#Keep nests above the cutoff
for i in range(breakpoint):
returnNests.append(sortList[i][0])
returnFitness.append(sortList[i][1])
returnReps.append(sortList[i][2])
newEvals = 0
newBestFitness = fmin
newBestNest = bestNest
newBestReps = fminReps
#Replace nests below cutoff and calculate their fitness
for i in range(breakpoint, numNests):
newNest = util.uniform_random(paramRanges)
newNest = util.boundary_clamp(newNest, paramRanges)
#Calculate fitness of new nest
fitnessReps = []
for j in range(repetitions):
currFitness,_,_,_ = evalFun(evalConstParams, newNest)
newEvals += 1
fitnessReps.append(currFitness)
newFitness = np.mean(fitnessReps)
#Add new nest to lists
returnNests.append(newNest)
returnFitness.append(newFitness)
returnReps.append(fitnessReps)
#Check if global bests need to be changed
if returnFitness[i] < newBestFitness:
newBestFitness = returnFitness[i]
newBestNest = returnNests[i]
newBestReps = returnReps[i]
return (newBestNest,newBestFitness,newBestReps,returnNests,returnFitness,returnReps,newEvals)
def cuckoo_paper(constParams, tuneParams):
evalFun = constParams[0]
evalConstParams = constParams[1]
paramRanges = constParams[2]
stopCond = constParams[3]
stopVal = constParams[4]
repetitions = constParams[5]
verbose = constParams[6]
numNests = tuneParams[0]
pa = tuneParams[1]
numEvals = 0
nests = [util.uniform_random(paramRanges) for i in range(numNests)]
fitness = [sys.float_info.max for i in range(numNests)]
fitnessReps = [[sys.float_info.max for j in range(repetitions)] for i in range(numNests)]
bestNest, fmin, fminReps, nests, fitness, fitnessReps, newEvals = find_best_nests(evalFun, evalConstParams, nests, nests, fitness, fitnessReps, repetitions)
numEvals += newEvals
iterations = 0
stopLoop = False
while(not stopLoop):
#Do single Levy flight
newBestNest, fnew, fnewReps, nests, fitness, fitnessReps, newEvals = levy_flight_paper(nests, bestNest, paramRanges, fmin, fminReps, evalFun, evalConstParams, fitness, fitnessReps, repetitions)
numEvals += newEvals
#Determine new best solution
if fnew < fmin:
fmin = fnew
bestNest = newBestNest
fminReps = fnewReps
#if repetitions > 1:
# print "**********"
#Remove proportion pa of lowest quality nests
newBestNest, fnew, fnewReps, nests, fitness, fitnessReps, newEvals = empty_nests_paper(nests, pa, bestNest, paramRanges, fmin, fminReps, evalFun, evalConstParams, fitness, fitnessReps, repetitions)
numEvals += newEvals
#if repetitions > 1:
# print "**********"
#Determine new best solution
if fnew < fmin:
fmin = fnew
bestNest = newBestNest
fminReps = fnewReps
iterations += 1
if stopCond == "fitness":
if fmin < stopVal[0]:
stopLoop = True
elif stopCond == "fitnessOrEvals":
if fmin < stopVal[0] or numEvals > stopVal[1]:
stopLoop = True
elif stopCond == "fitnessOrGlobalEvals":
if fmin < stopVal[0] or test_funs.numEvaluations > stopVal[1]:
stopLoop = True
elif stopCond == "generations":
if iterations >= stopVal[0]:
stopLoop = True
else:
print "ERROR: Stop condition not recognized"
break
if verbose:
print "Iteration: " + str(iterations)
print "Evaluations: " + str(numEvals)
print "Best Solution: " + str(bestNest)
print "Best Mean Fitness: " + str(fmin)
print "Best Fitness Set: " + str(fminReps) + "\n"
return (numEvals, bestNest, fmin, fminReps)
def uniform_random_search(constParams, tuneParams):
evalFun = constParams[0]
evalConstParams = constParams[1]
paramRanges = constParams[2]
stopCond = constParams[3]
stopVal = constParams[4]
repetitions = constParams[5]
verbose = constParams[6]
bestSol = None
bestSolFitness = sys.float_info.max
bestSolReps = None
stopLoop = False
numDim = len(paramRanges)
numEvals = 0
iterations = 0
while (not stopLoop):
newSol = util.uniform_random(paramRanges)
#Determine fitness of new solution
fitnessReps = []
for j in range(repetitions):
currFitness, _, _, _ = evalFun(evalConstParams, newSol)
numEvals += 1
fitnessReps.append(currFitness)
newFitness = np.mean(fitnessReps)
#Check if global bests need to be changed
if newFitness < bestSolFitness:
bestSolFitness = newFitness
bestSol = newSol
bestSolReps = fitnessReps
iterations += 1
if stopCond == "fitness":
if bestSolFitness < stopVal[0]:
stopLoop = True
elif stopCond == "fitnessOrEvals":
if bestSolFitness < stopVal[0] or numEvals > stopVal[1]:
stopLoop = True
elif stopCond == "fitnessOrGlobalEvals":
if bestSolFitness < stopVal[
0] or test_funs.numEvaluations > stopVal[1]:
stopLoop = True
elif stopCond == "generations":
if float(iterations) / 25.0 >= stopVal[0]:
stopLoop = True
else:
print "ERROR: Stop condition not recognized"
break
if verbose:
print "Iterations: " + str(iterations)
print "Evaluations: " + str(numEvals)
print "Best Solution: " + str(bestSol)
print "Best Mean Fitness: " + str(bestSolFitness)
print "Best Fitness Set: " + str(bestSolReps) + "\n"
return (numEvals, bestSol, bestSolFitness, bestSolReps)
def pso_basic(constParams, tuneParams):
evalFun = constParams[0]
evalConstParams = constParams[1]
paramRanges = constParams[2]
stopCond = constParams[3]
stopVal = constParams[4]
repetitions = constParams[5]
verbose = constParams[6]
numParticles = tuneParams[0]
inertia = tuneParams[1]
selfAdjust = tuneParams[2]
socialAdjust = tuneParams[3]
numDim = len(paramRanges)
initPosns = [util.uniform_random(paramRanges) for i in range(numParticles)]
paramWidths = [
abs(paramRanges[i][-1] - paramRanges[i][0]) for i in range(numDim)
]
velocityRanges = [[-paramWidths[i], paramWidths[i]] for i in range(numDim)]
initVelocities = [
util.uniform_random(velocityRanges) for i in range(numParticles)
]
particles = [
Particle(initPosns[i], initVelocities[i]) for i in range(numParticles)
]
bestSol = None
bestSolFitness = sys.float_info.max
bestSolReps = None
stopLoop = False
numEvals = 0
iterations = 0
bestSol, bestSolFitness, bestSolReps, newEvals = evaluate_sols(
evalFun, evalConstParams, particles, repetitions, bestSol,
bestSolFitness, bestSolReps)
numEvals += newEvals
while (not stopLoop):
for i in range(numParticles):
update_particles(particles, bestSol, inertia, selfAdjust,
socialAdjust, paramRanges, velocityRanges)
bestSol, bestSolFitness, bestSolReps, newEvals = evaluate_sols(
evalFun, evalConstParams, particles, repetitions, bestSol,
bestSolFitness, bestSolReps)
numEvals += newEvals
iterations += 1
if stopCond == "fitness":
if bestSolFitness < stopVal[0]:
stopLoop = True
elif stopCond == "fitnessOrEvals":
if bestSolFitness < stopVal[0] or numEvals > stopVal[1]:
stopLoop = True
elif stopCond == "fitnessOrGlobalEvals":
if bestSolFitness < stopVal[
0] or test_funs.numEvaluations > stopVal[1]:
stopLoop = True
elif stopCond == "generations":
if float(iterations) / 25.0 >= stopVal[0]:
stopLoop = True
else:
print "ERROR: Stop condition not recognized"
break
if verbose:
print "Iteration: " + str(iterations)
print "Evaluations: " + str(numEvals)
print "Best Solution: " + str(bestSol)
print "Best Mean Fitness: " + str(bestSolFitness)
print "Best Fitness Set: " + str(bestSolReps) + "\n"
return (numEvals, bestSol, bestSolFitness, bestSolReps)