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
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