Python Shekel Exemples

Exemple #1

Fichier : main.py Projet : ZongSingHuang/Enhanced-Whale-Optimization-Algorithm

    Shekel5 = functools.partial(benchmark.Shekel, m=5)
    ub = bound_X.Shekel()[1] * np.ones(dimension.Shekel())
    lb = bound_X.Shekel()[0] * np.ones(dimension.Shekel())
    optimizer = EWOA(fitness=Shekel5,
                     D=dimension.Shekel(),
                     P=P,
                     G=G,
                     ub=ub,
                     lb=lb)
    st = time.time()
    optimizer.opt()
    ed = time.time()
    F_table[t, item] = optimizer.gbest_F
    table[item]['avg'] += optimizer.gbest_F
    table[item]['time'] += ed - st
    table[item]['ideal'] = ideal_F.Shekel()
    loss_curves[:, item] += optimizer.loss_curve

    item = item + 1
    ub = bound_X.Branin()[2:] * np.ones(dimension.Branin())
    lb = bound_X.Branin()[:2] * np.ones(dimension.Branin())
    optimizer = EWOA(fitness=benchmark.Branin,
                     D=dimension.Branin(),
                     P=P,
                     G=G,
                     ub=ub,
                     lb=lb)
    st = time.time()
    optimizer.opt()
    ed = time.time()
    F_table[t, item] = optimizer.gbest_F

Exemple #2

Fichier : main.py Projet : ZongSingHuang/Multi-Strategy-Enhanced-Whale-Optimization-Algorithm

    Shekel5 = functools.partial(benchmark.Shekel, m=5)
    ub = bound_X.Shekel()[1] * np.ones(dimension.Shekel())
    lb = bound_X.Shekel()[0] * np.ones(dimension.Shekel())
    optimizer = MSEWOA(fitness=Shekel5,
                       D=dimension.Shekel(),
                       P=P,
                       G=G,
                       ub=ub,
                       lb=lb)
    st = time.time()
    optimizer.opt()
    ed = time.time()
    F_table[t, item] = optimizer.gbest_F
    table[item]['avg'] += optimizer.gbest_F
    table[item]['time'] += ed - st
    table[item]['ideal'] = ideal_F.Shekel(m=5)
    loss_curves[:, item] += optimizer.loss_curve

    item = item + 1
    ub = bound_X.Branin()[2:] * np.ones(dimension.Branin())
    lb = bound_X.Branin()[:2] * np.ones(dimension.Branin())
    optimizer = MSEWOA(fitness=benchmark.Branin,
                       D=dimension.Branin(),
                       P=P,
                       G=G,
                       ub=ub,
                       lb=lb)
    st = time.time()
    optimizer.opt()
    ed = time.time()
    F_table[t, item] = optimizer.gbest_F