metaheuristic_designer.simple.simulated_annealing module#
Ready-to-run Simulated Annealing wrappers.
- simulated_annealing_binary(objfunc, mutated_bits=1, initial_temperature=1.0, alpha=0.997, iterations=100, encoding=None, rng=None, **kwargs)[source]#
Simulated Annealing for binary-coded vectors.
- Return type:
- Parameters:
- objfuncObjectiveFunc
The objective function to optimize.
- mutated_bitsint, optional
Number of bits flipped per mutation (default 1).
- initial_temperaturefloat, optional
Starting temperature (default 1.0).
- alphafloat, optional
Cooling factor per iteration (default 0.997).
- iterationsint, optional
Number of iterations at constant temperature (default 100).
- encodingEncoding, optional
Encoding; defaults to
TypeCastEncoding(int → bool).- rngRNGLike, optional
Random seed or generator.
- **kwargs
Forwarded to
Algorithm.
- Parameters:
objfunc (ObjectiveFunc)
mutated_bits (int)
initial_temperature (float)
alpha (float)
iterations (int)
encoding (Encoding | None)
rng (int | Generator | None)
- simulated_annealing_permutation(objfunc, swapped_positions=2, initial_temperature=1.0, alpha=0.997, iterations=100, encoding=None, rng=None, **kwargs)[source]#
Simulated Annealing for permutation-coded vectors.
- Return type:
- Parameters:
- objfuncObjectiveFunc
The objective function to optimize.
- swapped_positionsint, optional
Number of positions swapped per mutation (default 2).
- initial_temperaturefloat, optional
Starting temperature (default 1.0).
- alphafloat, optional
Cooling factor per iteration (default 0.997).
- iterationsint, optional
Number of iterations at constant temperature (default 100).
- encodingEncoding, optional
Encoding applied to the genotype.
- rngRNGLike, optional
Random seed or generator.
- **kwargs
Forwarded to
Algorithm.
- Parameters:
objfunc (ObjectiveFunc)
swapped_positions (int)
initial_temperature (float)
alpha (float)
iterations (int)
encoding (Encoding | None)
rng (int | Generator | None)
- simulated_annealing_discrete(objfunc, resampled_components=1, initial_temperature=1.0, alpha=0.997, iterations=100, encoding=None, rng=None, **kwargs)[source]#
Simulated Annealing for integer-coded vectors.
- Return type:
- Parameters:
- objfuncObjectiveFunc
The objective function to optimize.
- resampled_componentsint, optional
Number of components resampled per mutation (default 1).
- initial_temperaturefloat, optional
Starting temperature (default 1.0).
- alphafloat, optional
Cooling factor per iteration (default 0.997).
- iterationsint, optional
Number of iterations at constant temperature (default 100).
- encodingEncoding, optional
Encoding applied to the genotype.
- rngRNGLike, optional
Random seed or generator.
- **kwargs
Forwarded to
Algorithm.
- Parameters:
objfunc (ObjectiveFunc)
resampled_components (int)
initial_temperature (float)
alpha (float)
iterations (int)
encoding (Encoding | None)
rng (int | Generator | None)
- simulated_annealing_real(objfunc, mutation_strength=0.01, mutated_components=1, initial_temperature=1.0, alpha=0.997, iterations=100, encoding=None, rng=None, **kwargs)[source]#
Simulated Annealing for real-coded vectors.
- Return type:
- Parameters:
- objfuncObjectiveFunc
The objective function to optimize.
- mutation_strengthfloat, optional
Standard deviation of Gaussian mutation (default 1e-2).
- mutated_componentsint, optional
Number of components mutated per individual (default 1).
- initial_temperaturefloat, optional
Starting temperature (default 1.0).
- alphafloat, optional
Cooling factor per iteration (default 0.997).
- iterationsint, optional
Number of iterations at constant temperature (default 100).
- encodingEncoding, optional
Encoding applied to the genotype.
- rngRNGLike, optional
Random seed or generator.
- **kwargs
Forwarded to
Algorithm.
- Parameters:
objfunc (ObjectiveFunc)
mutation_strength (float)
mutated_components (int)
initial_temperature (float)
alpha (float)
iterations (int)
encoding (Encoding | None)
rng (int | Generator | None)