def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=50000, benchmark=MyBenchmark())
algo = HarmonySearch(HMS=50,
r_accept=0.7,
r_pa=0.2,
b_range=1.1,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = EvolutionStrategyMp1(mu=65,
k=25,
c_a=1.5,
c_r=0.25,
seed=None,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = ParticleSwarmAlgorithm(NP=50,
C1=2.0,
C2=2.0,
w=0.5,
vMin=-5,
vMax=5,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = SelfAdaptiveDifferentialEvolutionAlgorithm(NP=10,
F=0.5,
F_l=-1,
F_u=2.0,
Tao1=0.1,
CR=0.45,
Tao2=0.25,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = CamelAlgorithm(NP=50,
omega=0.25,
alpha=0.15,
mu=0.5,
S_init=1,
E_init=1,
T_min=0,
T_max=100,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = GlowwormSwarmOptimizationV2(n=50,
nt=5,
l0=5,
rho=0.4,
gamma=0.6,
beta=0.08,
s=0.03,
seed=None,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(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 = MonkeyKingEvolutionV2(NP=25, C=3, F=0.5, FC=0.5, R=0.4, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(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 = FireworksAlgorithm(seed=seed, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(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 = MultipleTrajectorySearch(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(D=10,
nFES=50000,
seed=None,
optType=OptimizationType.MINIMIZATION,
optFunc=MinMB):
task = TaskConvPlot(D=D,
nFES=nFES,
nGEN=10000,
optType=optType,
benchmark=optFunc())
algo = GravitationalSearchAlgorithm(NP=40,
F=0.5,
CR=0.9,
seed=seed,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(D=10,
nFES=50000,
seed=None,
optType=OptimizationType.MINIMIZATION,
optFunc=MinMB,
**no):
task = TaskConvPlot(D=D,
nFES=nFES,
nGEN=10000,
optType=optType,
benchmark=optFunc())
algo = FireflyAlgorithm(NP=20,
alpha=0.5,
betamin=0.2,
gamma=1.0,
seed=seed,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = KrillHerdV11(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = BareBonesFireworksAlgorithm(task=task, n=15, C_a=1, C_r=0.5)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = GeneticAlgorithm(NP=40, Ts=5, Mr=0.5, Cr=0.4, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = SineCosineAlgorithm(NP=35, a=7, Rmin=0.1, Rmax=3, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')