optFunc=MinMB,
**kn):
NP = 120
task = TaskConvPlot(D=D,
nFES=nFES,
nGEN=nGEN,
optType=optType,
benchmark=optFunc())
algo = alg(seed=seed[0], task=task, Np=NP)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def getOptType(otype):
if otype == OptimizationType.MINIMIZATION: return MinMB
elif otype == OptimizationType.MAXIMIZATION: return MaxMB
else: return None
if __name__ == '__main__':
pargs, algo = getDictArgs(sys.argv[1:]), Runner.getAlgorithm('DE')
optFunc = getOptType(pargs['optType'])
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'log':
logging_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'plot':
plot_example(algo, optFunc=optFunc, **pargs)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
Ackley,
Griewank,
Sphere,
HappyCat
)
"""Example demonstrating the use of NiaPy Runner."""
runner = Runner(
D=40,
nFES=100,
nRuns=2,
useAlgorithms=[
GreyWolfOptimizer(),
"FlowerPollinationAlgorithm",
ParticleSwarmAlgorithm(),
"HybridBatAlgorithm",
"SimulatedAnnealing",
"CuckooSearch"],
useBenchmarks=[
Ackley(),
Griewank(),
Sphere(),
HappyCat(),
"rastrigin"]
)
print(runner.run(verbose=True))
func = getCecBench(cec, D)
task = TaskConvPrint(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc(func, sr[0], sr[1], fnum))
algo = alg(seed=seed[0], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def plot_example(alg, cec, fnum=1, D=10, nFES=50000, nGEN=5000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, wout=False, sr=[-100, 100], **kwu):
func = getCecBench(cec, D)
task = TaskConvPlot(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc(func, sr[0], sr[1], fnum))
algo = alg(seed=seed[0], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def getOptType(otype):
if otype == OptimizationType.MINIMIZATION: return MinMB
elif otype == OptimizationType.MAXIMIZATION: return MaxMB
else: return None
if __name__ == '__main__':
pargs = getDictArgs(sys.argv[1:])
pargs['nFES'] = round(pargs['D'] * getMaxFES(pargs['cec']) * pargs['reduc'])
algo = Runner.getAlgorithm(pargs['algo'])
optFunc = getOptType(pargs['optType'])
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'log': logging_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'plot': plot_example(algo, optFunc=optFunc, **pargs)
else: simple_example(algo, optFunc=optFunc, **pargs)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def simple_example(alg, runs=2, D=10, nFES=50000, nGEN=10000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, benchmark='ackley', **kn):
for i in range(runs):
algo = Runner.getAlgorithm('GA')(D=10, NP=55, nFES=nFES, nGEN=nGEN, A=0.5, r=0.5, Qmin=0.0, Qmax=2.0, benchmark=benchmark, seed=seed[i % len(seed)])
best = algo.run()
logger.info('%s \n %s %s' % (algo.task.unused_evals(), best[0], best[1]))
def evaluate(D, sol):
val = 0.0
for i in range(D):
val = val + sol[i] * sol[i]
return val
return evaluate
# example using custom benchmark "MyBenchmark"
logger.info('Running with custom MyBenchmark...')
for i in range(10):
Algorithm = Runner.getAlgorithm('BA')(D=10,
NP=40,
nFES=50000,
A=0.5,
r=0.5,
Qmin=0.0,
Qmax=2.0,
benchmark=MyBenchmark())
Best = Algorithm.run()
logger.info(Best)
# example using predifined benchmark function
# available benchmarks are:
# - griewank
# - rastrigin
# - rosenbrock
# - sphere
logger.info('Running with default Griewank benchmark...')
griewank = Griewank()
optFunc=MinMB,
**kn):
task = TaskConvPlot(D=D,
nFES=nFES,
nGEN=nGEN,
optType=optType,
benchmark=optFunc())
algo = alg(seed=seed[0], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def getOptType(otype):
if otype == OptimizationType.MINIMIZATION: return MinMB
elif otype == OptimizationType.MAXIMIZATION: return MaxMB
else: return None
if __name__ == '__main__':
pargs, algo = getDictArgs(
sys.argv[1:]), Runner.getAlgorithm('EnhancedFireworksAlgorithm')
optFunc = getOptType(pargs['optType'])
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'log':
logging_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'plot':
plot_example(algo, optFunc=optFunc, **pargs)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
optFunc=MinMB,
**kn):
task = TaskConvPlot(D=D,
nFES=nFES,
nGEN=nGEN,
optType=optType,
benchmark=optFunc())
algo = alg(seed=seed, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def getOptType(otype):
if otype == OptimizationType.MINIMIZATION: return MinMB
elif otype == OptimizationType.MAXIMIZATION: return MaxMB
else: return None
if __name__ == '__main__':
pargs, algo = getDictArgs(sys.argv[1:]), Runner.getAlgorithm(
'CovarianceMaatrixAdaptionEvolutionStrategy')
optFunc = getOptType(pargs['optType'])
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'log':
logging_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'plot':
plot_example(algo, optFunc=optFunc, **pargs)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
algo = alg(seed=seed[i % len(seed)], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def logging_example(alg, D=10, nFES=50000, nGEN=100000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn):
task = TaskConvPrint(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc())
algo = alg(seed=seed[0], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def plot_example(alg, D=10, nFES=50000, nGEN=100000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc())
algo = alg(seed=seed[0], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def getOptType(otype):
if otype == OptimizationType.MINIMIZATION: return MinMB
elif otype == OptimizationType.MAXIMIZATION: return MaxMB
else: return None
if __name__ == '__main__':
pargs, algo = getDictArgs(sys.argv[1:]), Runner.getAlgorithm('DynamicFireworksAlgorithmGauss')
optFunc = getOptType(pargs['optType'])
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'log': logging_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'plot': plot_example(algo, optFunc=optFunc, **pargs)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
optFunc=MinMB,
**kn):
task = TaskConvPlot(D=D,
nFES=nFES,
nGEN=nGEN,
optType=optType,
benchmark=optFunc())
algo = alg(seed=seed[0], task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def getOptType(otype):
if otype == OptimizationType.MINIMIZATION: return MinMB
elif otype == OptimizationType.MAXIMIZATION: return MaxMB
else: return None
if __name__ == '__main__':
pargs, algo = getDictArgs(sys.argv[1:]), Runner.getAlgorithm(
'DynNPSelfAdaptiveDifferentialEvolutionAlgorithm')
optFunc = getOptType(pargs['optType'])
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'log':
logging_example(algo, optFunc=optFunc, **pargs)
elif pargs['runType'] == 'plot':
plot_example(algo, optFunc=optFunc, **pargs)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3