PymooOptimizer

class PymooOptimizer(method, population_size=50, **kwargs)

Bases: Optimizer

Optimizer wrapper for pymoo optimization algorithms and CMA-ES.

Supports population-based optimization methods from the pymoo library (DE) and the cma library (CMAES).

Initialize a pymoo-based optimizer.

Parameters:

• method (PymooMethod) – The optimization algorithm to use (CMAES or DE).

• population_size (int) – Size of the population for the algorithm. Defaults to 50.

• **kwargs – Additional algorithm-specific parameters passed to pymoo/cma.

Attributes Summary

n_param_sets

Get the number of parameter sets (population size) used by this optimizer.

Methods Summary

copy()	Fresh copy, rebuilt from configuration (drops the pymoo algorithm state).
get_config()	Get optimizer configuration for checkpoint reconstruction.
load_state(checkpoint_dir)	Load the optimizer's internal state from a checkpoint directory.
optimize(cost_fn[, initial_params, callback_fn])	Run the optimization algorithm.
reset()	Reset the optimizer's internal state.
save_state(checkpoint_dir)	Save the optimizer's internal state to a checkpoint directory.

Attributes Documentation

n_param_sets

Get the number of parameter sets (population size) used by this optimizer.

Returns:

Population size for the optimization algorithm.

Return type:

int

Methods Documentation

copy()

Fresh copy, rebuilt from configuration (drops the pymoo algorithm state).

Return type:

PymooOptimizer

get_config()

Get optimizer configuration for checkpoint reconstruction.

Returns:

Dictionary containing optimizer type and configuration parameters.

Return type:

dict[str, Any]

classmethod load_state(checkpoint_dir)

Load the optimizer’s internal state from a checkpoint directory.

Creates a new PymooOptimizer instance with the state restored from the checkpoint.

Parameters:

checkpoint_dir (Path | str) – Directory path where the optimizer state is saved.

Returns:

A new optimizer instance with restored state.

Return type:

PymooOptimizer

Raises:

FileNotFoundError – If the checkpoint file does not exist.

optimize(cost_fn, initial_params=None, callback_fn=None, **kwargs)

Run the optimization algorithm.

Parameters:

• cost_fn (Callable[[ndarray[tuple[Any, ...], dtype[double]]], float | ndarray[tuple[Any, ...], dtype[double]]]) – Function to minimize. Should accept a 2D array of parameter sets and return an array of cost values.

• initial_params (ndarray[tuple[Any, ...], dtype[double]] | None) – Initial parameter values as a 2D array of shape (n_param_sets, n_params). Should be None when resuming from a checkpoint.

• callback_fn (Callable | None) – Function called after each iteration with an OptimizeResult object. Defaults to None.

• **kwargs –

  Additional keyword arguments:

  • max_iterations (int): Total desired number of iterations. When resuming from a checkpoint, this represents the total iterations desired across all runs. The optimizer will automatically calculate and run only the remaining iterations needed. Defaults to 5.

  • rng (np.random.Generator): Random number generator.

Returns:

Optimization result with final parameters and cost value.

Return type:

OptimizeResult

reset()

Reset the optimizer’s internal state.

Clears the current algorithm object, allowing the optimizer to be reused for fresh optimization runs.

Return type:

None

save_state(checkpoint_dir)

Save the optimizer’s internal state to a checkpoint directory.

Parameters:

checkpoint_dir (Path | str) – Directory path where the optimizer state will be saved.

Raises:

RuntimeError – If optimization has not been run (no state to save).

Return type:

None