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main.py
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from customized_optimizer.CEC2022.cec_pytorch_packaging import pytorch_cec2022_func
from customized_optimizer.MPA.marine_predators import MarinePredatorsAlgorithm
def main(case, dim, max_iter, early_stop=False):
if case == 1:
# uni-modal function : shifted and full rotated zakharov function (*300)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 2:
# uni-modal function : Shifted and full Rotated Rosenbrock’s Function (*400)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 3:
# uni-modal function : Shifted and full Rotated Expanded Schaffer’s f6 Function (*600)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=1000,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 4:
# uni-modal function : Shifted and full Rotated Non-Continuous Rastrigin’s Function (800)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=1000,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 5:
# 支持维度[2, 10, 20]
# uni-modal function : Shifted and full Rotated Levy Function (900)
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=1000,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 6:
# uni-modal function : Hybrid Function (1800)
# 支持维度[10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 7:
# Hybrid Functions : Hybrid Function 2 (2000)
# 支持维度[10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 8:
# Hybrid Functions : Hybrid Function 3 (2200)
# 支持维度[10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 9:
# Composition Functions : Composition Function 1 (2300)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 10:
# Composition Functions : Composition Function 2 (2400)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 11:
# Composition Functions : Composition Function 3 (2600)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if case == 12:
# Composition Functions : Composition Function 4 (2700)
# 支持维度[2, 10, 20]
dim = dim
lower_bound = [-100] * dim
upper_bound = [100] * dim
optimizer = MarinePredatorsAlgorithm()
func = pytorch_cec2022_func(func_num=case)
c, d = optimizer.optimizing(fitness_function=func,
dimension=dim, lower_bound=lower_bound, upper_bound=upper_bound,
population_size=50,
max_iteration=max_iter,
early_stop=early_stop, tolerance=1e-6, n_no_improvement=10)
if __name__ == '__main__':
main(case=8, dim=10, max_iter=2000, early_stop=False)