mealpy.system_based package

mealpy.system_based.AEO module

class mealpy.system_based.AEO.AugmentedAEO(epoch: int = 10000, pop_size: int = 100, **kwargs: object)[source]

Bases: mealpy.optimizer.Optimizer

The original version of: Augmented Artificial Ecosystem Optimization (AAEO)

Notes

  • Used linear weight factor reduce from 2 to 0 through time

  • Applied Levy-flight technique and the global best solution

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = AEO.AugmentedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

class mealpy.system_based.AEO.EnhancedAEO(epoch: int = 10000, pop_size: int = 100, **kwargs: object)[source]

Bases: mealpy.optimizer.Optimizer

The original version of: Enhanced Artificial Ecosystem-Based Optimization (EAEO)

Links:

  1. https://doi.org/10.1109/ACCESS.2020.3027654

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = AEO.EnhancedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Eid, A., Kamel, S., Korashy, A. and Khurshaid, T., 2020. An enhanced artificial ecosystem-based optimization for optimal allocation of multiple distributed generations. IEEE Access, 8, pp.178493-178513.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

class mealpy.system_based.AEO.ImprovedAEO(epoch: int = 10000, pop_size: int = 100, **kwargs: object)[source]

Bases: mealpy.system_based.AEO.OriginalAEO

The original version of: Improved Artificial Ecosystem-based Optimization (ImprovedAEO)

Links:

  1. https://doi.org/10.1016/j.ijhydene.2020.06.256

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = AEO.ImprovedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Rizk-Allah, R.M. and El-Fergany, A.A., 2021. Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy, 46(75), pp.37612-37627.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

class mealpy.system_based.AEO.ModifiedAEO(epoch: int = 10000, pop_size: int = 100, **kwargs: object)[source]

Bases: mealpy.optimizer.Optimizer

The original version of: Modified Artificial Ecosystem-Based Optimization (MAEO)

Links:

  1. https://doi.org/10.1109/ACCESS.2020.2973351

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = AEO.ModifiedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Menesy, A.S., Sultan, H.M., Korashy, A., Banakhr, F.A., Ashmawy, M.G. and Kamel, S., 2020. Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, pp.31892-31909.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

class mealpy.system_based.AEO.OriginalAEO(epoch: int = 10000, pop_size: int = 100, **kwargs: object)[source]

Bases: mealpy.optimizer.Optimizer

The original version of: Artificial Ecosystem-based Optimization (AEO)

Links:

  1. https://doi.org/10.1007/s00521-019-04452-x

  2. https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = AEO.OriginalAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Zhao, W., Wang, L. and Zhang, Z., 2020. Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 32(13), pp.9383-9425.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

mealpy.system_based.GCO module

class mealpy.system_based.GCO.DevGCO(epoch: int = 10000, pop_size: int = 100, cr: float = 0.7, wf: float = 1.25, **kwargs: object)[source]

Bases: mealpy.optimizer.Optimizer

The developed version: Germinal Center Optimization (GCO)

Notes

  • The global best solution and 2 random solutions are used instead of randomizing 3 solutions

Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:

  • cr (float): [0.5, 0.95], crossover rate, default = 0.7 (Same as DE algorithm)

  • wf (float): [1.0, 2.0], weighting factor (f in the paper), default = 1.25 (Same as DE algorithm)

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, GCO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = GCO.DevGCO(epoch=1000, pop_size=50, cr = 0.7, wf = 1.25)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

initialize_variables()[source]
class mealpy.system_based.GCO.OriginalGCO(epoch: int = 10000, pop_size: int = 100, cr: float = 0.7, wf: float = 1.25, **kwargs: object)[source]

Bases: mealpy.system_based.GCO.DevGCO

The original version of: Germinal Center Optimization (GCO)

Links:

  1. https://doi.org/10.2991/ijcis.2018.25905179

  2. https://www.atlantis-press.com/journals/ijcis/25905179/view

Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:

  • cr (float): [0.5, 0.95], crossover rate, default = 0.7 (Same as DE algorithm)

  • wf (float): [1.0, 2.0], weighting factor (f in the paper), default = 1.25 (Same as DE algorithm)

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, GCO
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = GCO.OriginalGCO(epoch=1000, pop_size=50, cr = 0.7, wf = 1.25)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Villaseñor, C., Arana-Daniel, N., Alanis, A.Y., López-Franco, C. and Hernandez-Vargas, E.A., 2018. Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), p.13.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

mealpy.system_based.WCA module

class mealpy.system_based.WCA.OriginalWCA(epoch: int = 10000, pop_size: int = 100, nsr: int = 4, wc: float = 2.0, dmax: float = 1e-06, **kwargs: object)[source]

Bases: mealpy.optimizer.Optimizer

The original version of: Water Cycle Algorithm (WCA)

Links:

  1. https://doi.org/10.1016/j.compstruc.2012.07.010

Notes

The ideas are (almost the same as ICO algorithm):

  • 1 sea is global best solution

  • a few river which are second, third, …

  • other left are stream (will flow directed to sea or river)

Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:

  • nsr (int): [4, 10], Number of rivers + sea (sea = 1), default = 4

  • wc (float): [1.0, 3.0], Weighting coefficient (C in the paper), default = 2

  • dmax (float): [1e-6], fixed parameter, Evaporation condition constant, default=1e-6

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, WCA
>>>
>>> def objective_function(solution):
>>>     return np.sum(solution**2)
>>>
>>> problem_dict = {
>>>     "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>>     "minmax": "min",
>>>     "obj_func": objective_function
>>> }
>>>
>>> model = WCA.OriginalWCA(epoch=1000, pop_size=50, nsr = 4, wc = 2.0, dmax = 1e-6)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")

References

[1] Eskandar, H., Sadollah, A., Bahreininejad, A. and Hamdi, M., 2012. Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, pp.151-166.

evolve(epoch)[source]

The main operations (equations) of algorithm. Inherit from Optimizer class

Parameters

epoch (int) – The current iteration

initialization()[source]