mealpy.music_based package

mealpy.music_based.HS module

class mealpy.music_based.HS.DevHS(epoch: int = 10000, pop_size: int = 100, c_r: float = 0.95, pa_r: float = 0.05, **kwargs: object)

Bases: mealpy.optimizer.Optimizer

The developed version: Harmony Search (HS)

Links:

1. https://doi.org/10.1177/003754970107600201

Notes

• Used the global best in the harmony memories

• Removed all third for loops

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

• c_r (float): [0.1, 0.5], Harmony Memory Consideration Rate), default = 0.15

• pa_r (float): [0.3, 0.8], Pitch Adjustment Rate, default=0.5

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, HS
>>>
>>> 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 = HS.DevHS(epoch=1000, pop_size=50, c_r = 0.95, pa_r = 0.05)
>>> 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)

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

Parameters

epoch (int) – The current iteration

initialize_variables()

class mealpy.music_based.HS.OriginalHS(epoch: int = 10000, pop_size: int = 100, c_r: float = 0.95, pa_r: float = 0.05, **kwargs: object)

Bases: mealpy.music_based.HS.DevHS

The original version of: Harmony Search (HS)

Links:

1. https://doi.org/10.1177/003754970107600201

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

• c_r (float): [0.1, 0.5], Harmony Memory Consideration Rate), default = 0.15

• pa_r (float): [0.3, 0.8], Pitch Adjustment Rate, default=0.5

Examples

>>> import numpy as np
>>> from mealpy import FloatVar, HS
>>>
>>> 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 = HS.OriginalHS(epoch=1000, pop_size=50, c_r = 0.95, pa_r = 0.05)
>>> 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] Geem, Z.W., Kim, J.H. and Loganathan, G.V., 2001. A new heuristic optimization algorithm: harmony search. simulation, 76(2), pp.60-68.

evolve(epoch)

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

Parameters

epoch (int) – The current iteration