def test_getDictArgs_fine(self): args = getDictArgs(['-D', '10', '-nFES', '100000000', '-a', 'SCA']) self.assertTrue(args) self.assertEqual(args['D'], 10) self.assertEqual(args['nFES'], 100000000) self.assertEqual(args['algo'], 'SCA') self.assertEqual(args['seed'], [None])
def test_getDictArgs_seed_fine(self): args = getDictArgs([ '-D', '10', '-nFES', '100000000', '-a', 'SCA', '-seed', '1', '234', '231523' ]) self.assertTrue(args) self.assertEqual(args['D'], 10) self.assertEqual(args['nFES'], 100000000) self.assertEqual(args['algo'], 'SCA') self.assertEqual(args['seed'], [1, 234, 231523])
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:]), SineCosineAlgorithm 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:]), GravitationalSearchAlgorithm 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
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:]), MonkeyKingEvolutionV1 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
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:]), FireworksAlgorithm 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
optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn): task = TaskConvPlot(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc()) algo = alg(seed=seed[0]) best = algo.run(task) 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:]), EvolutionStrategyML 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
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:]), MultipleTrajectorySearch 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('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
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:]), FireflyAlgorithm 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
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:]), SimulatedAnnealing 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
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:]), ForestOptimizationAlgorithm 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)
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:]), CamelAlgorithm 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
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:]), NelderMeadMethod 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): 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
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:]), ParticleSwarmAlgorithm optFunc = getOptType(pargs['optType']) pargs.pop('nFES', None), pargs.pop('nGEN', None) 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
optType=OptimizationType.MINIMIZATION, 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:]), GlowwormSwarmOptimizationV3 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, 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, 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, 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:]), HarmonySearchV1 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, 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, 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, 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:]), TabuSearch 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:]), GeneticAlgorithm 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
optType=OptimizationType.MINIMIZATION, 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:]), HillClimbAlgorithm 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
optType=OptimizationType.MINIMIZATION, 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:]), KrillHerdV4 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