metaheuristic_designer.strategies.classic.SA

class SA(initializer, operator, name='SA', iterations=100, temperature_init=100, alpha=0.99, rng=None, **kwargs)

Simulated Annealing algorithm.

A single solution is perturbed each iteration. The new solution is accepted if it is better, or probabilistically if it is worse, according to an exponentially decaying temperature schedule.

Parameters:

initializerInitializer

Population initializer (usually creates a single individual).

operatorOperator

Perturbation operator.

namestr, optional

Display name (default "SA").

iterationsint or SchedulableParameter, optional

Number of iterations at constant temperature (default 100).

temperature_initfloat or SchedulableParameter, optional

Starting temperature (default 100).

alphafloat or SchedulableParameter, optional

Cooling factor (default 0.99).

rngRNGLike, optional

Random number generator.

**kwargs

Forwarded to HillClimb.

Attributes:

params

Access parameter values by attribute-style lookup.

population_size

Gets the amount of individuals in the population.

temperature

Parameters:

• initializer (Initializer)

• operator (Operator)

• name (str)

• iterations (int | SchedulableParameter)

• temperature_init (float | SchedulableParameter)

• alpha (float | SchedulableParameter)

• rng (Optional[RNGLike])

Methods

reset

__init__(initializer, operator, name='SA', iterations=100, temperature_init=100, alpha=0.99, rng=None, **kwargs)

Parameters:

• initializer (Initializer)

• operator (Operator)

• name (str)

• iterations (int | SchedulableParameter)

• temperature_init (float | SchedulableParameter)

• alpha (float | SchedulableParameter)

• rng (int | Generator | None)

Methods

__init__(initializer, operator[, name, ...])
extra_report()	Hook called at the end of the optimization (intended for subclasses).
extra_step_info()	Displays temperature values and acceptance probability.
gather_parameters()	Collect the current parameters from all sub-components.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Gets the current state of the search strategy as a dictionary.
initialize(objfunc)	Initializes the optimization search strategy.
reset(objfunc)
step(prev_population, objfunc)	Performs a single iteration of the algorithm on a given population.
store_kwargs([progress])	Store keyword arguments and evaluate them at the given progress.
update(progress)	Advances the state of the search by one iteration.
update_kwargs([progress])	Add or replace parameters and immediately evaluate them.