piglot.optimisers.pso.PSO

class PSO(objective: Objective, n_part, options, oh_strategy=None, bh_strategy='periodic', velocity_clamp=None, vh_strategy='unmodified', center=1.0, ftol_iter=1, n_processes=None)

Bases: ScalarOptimiser

PSO optimiser. Documentation: https://pyswarms.readthedocs.io/en/latest/_modules/pyswarms/single/global_best.html#GlobalBestPSO

Attributes

n_partint

number of particles in the swarm.

options : dict with keys {'c1', 'c2', 'w'} a dictionary containing the parameters for the specific optimization technique.

• c1float

  cognitive parameter

• c2float

  social parameter

• wfloat

  inertia parameter

oh_strategydict, optional, default=None(constant options)

a dict of update strategies for each option.

bh_strategystr

a strategy for the handling of out-of-bounds particles.

velocity_clamptuple, optional

a tuple of size 2 where the first entry is the minimum velocity and the second entry is the maximum velocity. It sets the limits for velocity clamping.

vh_strategystr

a strategy for the handling of the velocity of out-of-bounds particles.

centerlist (default is None)

an array of size dimensions

ftol_iterint

number of iterations over which the relative error in objective_func(best_pos) is acceptable for convergence. Default is 1

n_processesint

number of processes to use for parallel particle evaluation (default: None = no parallelization)

Methods

_optimise(self, func, n_dim, n_iter, bound, init_shot):

Solves the optimization problem

Methods

optimise

Optimiser for the outside world.

optimise(n_iter: int, parameters: ~piglot.parameter.ParameterSet, output_dir: str, stop_criteria: ~piglot.optimiser.StoppingCriteria = <piglot.optimiser.StoppingCriteria object>, verbose: bool = True) → Tuple[float, ndarray]

Optimiser for the outside world.

Parameters

objectiveObjective

Objective function to optimise.

n_iterint

Maximum number of iterations.

parametersParameterSet

Set of parameters to optimise.

output_dirstr

Whether to write output to the output directory, by default None.

stop_criteriaStoppingCriteria

List of stopping criteria, by default none attributed.

verbosebool

Whether to output progress status, by default True.

Returns

float

Best observed objective value.

np.ndarray

Observed optimum of the objective.