"""Ready-to-run Particle Swarm Optimization wrappers."""
from __future__ import annotations
from typing import Optional
from metaheuristic_designer.encoding import Encoding
from metaheuristic_designer.objective_function import ObjectiveFunc
from ..algorithm import Algorithm
from ..initializers import UniformInitializer
from ..encodings import TypeCastEncoding, SigmoidEncoding
from ..strategies import PSO
from ..utils import RNGLike, check_rng
[docs]
def particle_swarm_binary(
objfunc: ObjectiveFunc,
population_size: int = 100,
w: float = 0.7,
c1: float = 1.5,
c2: float = 1.5,
encoding: Optional[Encoding] = None,
rng: Optional[RNGLike] = None,
**kwargs,
) -> Algorithm:
"""Particle Swarm optimization for binary-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimize.
population_size : int, optional
Swarm size (default 100).
w : float, optional
Inertia weight (default 0.7).
c1 : float, optional
Cognitive acceleration coefficient (default 1.5).
c2 : float, optional
Social acceleration coefficient (default 1.5).
encoding : Encoding, optional
Encoding; defaults to :class:`SigmoidEncoding`.
rng : RNGLike, optional
Random seed or generator.
\\*\\*kwargs
Forwarded to :class:`Algorithm`.
"""
rng = check_rng(rng)
if encoding is None:
encoding = SigmoidEncoding(as_probability=False, threshold=0.5)
pop_initializer = UniformInitializer(
objfunc.dimension,
objfunc.lower_bound,
objfunc.upper_bound,
population_size=population_size,
dtype=float,
encoding=encoding,
rng=rng,
)
search_strat = PSO(
initializer=pop_initializer,
w=w,
c1=c1,
c2=c2,
rng=rng,
)
return Algorithm(objfunc, search_strat, **kwargs)
[docs]
def particle_swarm_discrete(
objfunc: ObjectiveFunc,
population_size: int = 100,
w: float = 0.7,
c1: float = 1.5,
c2: float = 1.5,
encoding: Optional[Encoding] = None,
rng: Optional[RNGLike] = None,
**kwargs,
) -> Algorithm:
"""Particle Swarm optimization for integer-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimize.
population_size : int, optional
Swarm size (default 100).
w : float, optional
Inertia weight (default 0.7).
c1 : float, optional
Cognitive acceleration coefficient (default 1.5).
c2 : float, optional
Social acceleration coefficient (default 1.5).
encoding : Encoding, optional
Encoding; defaults to :class:`TypeCastEncoding` (float → int).
rng : RNGLike, optional
Random seed or generator.
\\*\\*kwargs
Forwarded to :class:`Algorithm`.
"""
rng = check_rng(rng)
if encoding is None:
encoding = TypeCastEncoding(float, int)
pop_initializer = UniformInitializer(
objfunc.dimension,
objfunc.lower_bound,
objfunc.upper_bound,
population_size=population_size,
dtype=float,
encoding=encoding,
rng=rng,
)
search_strat = PSO(
initializer=pop_initializer,
w=w,
c1=c1,
c2=c2,
rng=rng,
)
return Algorithm(objfunc, search_strat, **kwargs)
[docs]
def particle_swarm_real(
objfunc: ObjectiveFunc,
population_size: int = 100,
w: float = 0.7,
c1: float = 1.5,
c2: float = 1.5,
encoding: Optional[Encoding] = None,
rng: Optional[RNGLike] = None,
**kwargs,
) -> Algorithm:
"""Particle Swarm Optimization for real-coded vectors.
Parameters
----------
objfunc : ObjectiveFunc
The objective function to optimize.
population_size : int, optional
Swarm size (default 100).
w : float, optional
Inertia weight (default 0.7).
c1 : float, optional
Cognitive acceleration coefficient (default 1.5).
c2 : float, optional
Social acceleration coefficient (default 1.5).
encoding : Encoding, optional
Encoding applied to the genotype.
rng : RNGLike, optional
Random seed or generator.
\\*\\*kwargs
Forwarded to :class:`Algorithm`.
"""
rng = check_rng(rng)
pop_initializer = UniformInitializer(
objfunc.dimension,
objfunc.lower_bound,
objfunc.upper_bound,
population_size=population_size,
dtype=float,
encoding=encoding,
rng=rng,
)
search_strat = PSO(
initializer=pop_initializer,
w=w,
c1=c1,
c2=c2,
rng=rng,
)
return Algorithm(objfunc, search_strat, **kwargs)