def logging_example(D=10, nFES=50000): task = TaskConvPrint(D=D, nFES=nFES, nGEN=50000, benchmark=MyBenchmark()) algo = HarmonySearch(HMS=50, r_accept=0.7, r_pa=0.2, b_range=1.1, seed=None, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(): task = TaskConvPrint(D=50, nFES=50000, nGEN=50000, benchmark=MyBenchmark()) algo = HarmonySearchV1(HMS=50, r_accept=0.7, r_pa=0.2, bw_min=0.32, bw_max=1.5, seed=None, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(): task = TaskConvPrint(D=50, nFES=50000, nGEN=50000, benchmark=MyBenchmark()) algo = EvolutionStrategyMp1(mu=50, k=25, c_a=1.5, c_r=0.25, seed=None, task=task) best = algo.run() logger.info('nFES:%s nGEN:%s\n%s %s' % (task.Evals, task.Iters, best[0], best[1]))
def logging_example(D=10, nFES=50000): task = TaskConvPrint(D=D, nFES=nFES, nGEN=50000, 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]))
def logging_example(): task = TaskConvPrint(D=50, nFES=50000, nGEN=50000, benchmark=MyBenchmark()) algo = ParticleSwarmAlgorithm(NP=50, C1=2.0, C2=2.0, w=0.5, vMin=-5, vMax=5, seed=None, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(): task = TaskConvPrint(D=50, nFES=50000, nGEN=50000, 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]))
def logging_example(D=10, nFES=50000): task = TaskConvPrint(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark()) algo = GlowwormSwarmOptimizationV2(n=25, nt=5, l0=5, rho=0.2, gamma=0.3, beta=0.05, s=0.05, seed=None, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(): task = TaskConvPrint(D=10, nFES=50000, nGEN=50000, benchmark=MyBenchmark()) algo = GeneticAlgorithm(NP=40, Ts=4, Mr=0.2, Cr=0.5, Selection=RouletteSelection, Mutation=MutationUros, Crossover=CrossoverUros, seed=None, task=task) # algo = GeneticAlgorithm(NP=50, Ts=10, Mr=0.5, Cr=0.5, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(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 = 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]))
def logging_example(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 = FireworksAlgorithm(seed=seed, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(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 = MultipleTrajectorySearch(task=task, n=15, C_a=1, C_r=0.5) best = algo.run() logger.info('%s %s' % (best[0], best[1]))
def logging_example(D=10, nFES=50000, seed=None, optType=OptimizationType.MINIMIZATION, optFunc=MinMB): task = TaskConvPrint(D=D, nFES=nFES, nGEN=50000, 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]))
def logging_example(D=10, nFES=50000, seed=None, optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **no): task = TaskConvPrint(D=D, nFES=nFES, nGEN=50000, 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]))
def logging_example(D=10, nFES=50000): task = TaskConvPrint(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]))
def logging_example(D=100, nFES=50000): task = TaskConvPrint(D=D, nFES=nFES, 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]))
def logging_example(): task = TaskConvPrint(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark()) algo = SineCosineAlgorithm(NP=35, a=3, Rmin=0.1, Rmax=1.1, task=task) best = algo.run() logger.info('%s %s' % (best[0], best[1]))