saealib.Optimizer

class saealib.Optimizer(problem)

Bases: object

Optimizer class for evolutionary algorithms.

Integrates problem definition, evolutionary algorithm, surrogate model, model manager, and termination condition, and manages the optimization process.

Parameters:

problem (Problem)

problem

The optimization problem.

Type:

Problem

algorithm

The evolutionary algorithm.

Type:

Algorithm

surrogate

The surrogate model.

Type:

Surrogate

strategy

The optimization strategy.

Type:

OptimizationStrategy

termination

The termination condition.

Type:

Termination

archive

The archive of evaluated solutions.

Type:

Archive

popsize

The population size.

Type:

int

seed

The random seed.

Type:

int

rng

The random number generator.

Type:

numpy.random.Generator

fe

The current number of function evaluations.

Type:

int

gen

The current generation number.

Type:

int

cbmanager

The callback event manager.

Type:

CallbackManager

instance_name

The name of the optimizer instance.

Type:

str

Methods

__init__	Initialize the Optimizer.
dispatch	Dispatch an event to the callback manager.
iterate	Iterate the optimization process.
run	Run the optimization process.
set_algorithm	Set the evolutionary algorithm.
set_evaluator	Set the evaluator.
set_initializer	Set the initializer.
set_instance_name	Set the instance name.
set_strategy	Set the optimization strategy.
set_surrogate	Wrap a raw Surrogate in a LocalSurrogateManager.
set_surrogate_manager	Set the surrogate manager.
set_termination	Set the termination condition.
validate	Check configuration consistency.

Method Details

Optimizer.__init__(problem)

Initialize the Optimizer.

Parameters:

problem (Problem) – The optimization problem.

Optimizer.dispatch(event)

Dispatch an event to the callback manager.

Parameters:

event (Event)

Return type:

None

Optimizer.iterate()

Iterate the optimization process.

Returns:

Generator of OptimizationContext.

Return type:

Generator[OptimizationContext]

Optimizer.run()

Run the optimization process.

Returns:

The optimization context.

Return type:

OptimizationContext

Optimizer.set_algorithm(algorithm)

Set the evolutionary algorithm. Returns self.

Parameters:

algorithm (Algorithm)

Return type:

Optimizer

Optimizer.set_evaluator(evaluator)

Set the evaluator. Returns self.

Parameters:

evaluator (Evaluator)

Return type:

Optimizer

Optimizer.set_initializer(initializer)

Set the initializer. Returns self.

Parameters:

initializer (Initializer)

Return type:

Optimizer

Optimizer.set_instance_name(name)

Set the instance name. Returns self.

Parameters:

name (str)

Return type:

Optimizer

Optimizer.set_strategy(strategy)

Set the optimization strategy. Returns self.

Parameters:

strategy (OptimizationStrategy)

Return type:

Optimizer

Optimizer.set_surrogate(surrogate, n_neighbors=50)

Wrap a raw Surrogate in a LocalSurrogateManager. Returns self.

Equivalent to set_surrogate_manager(LocalSurrogateManager(surrogate, ...)). Provided for backward compatibility.

Parameters:

• surrogate (Surrogate)

• n_neighbors (int)

Return type:

Optimizer

Optimizer.set_surrogate_manager(manager)

Set the surrogate manager. Returns self.

Parameters:

manager (SurrogateManager)

Return type:

Optimizer

Optimizer.set_termination(termination)

Set the termination condition. Returns self.

Parameters:

termination (Termination)

Return type:

Optimizer

Optimizer.validate()

Check configuration consistency. Returns list of issues.

Return type:

list[str]