Source code for metaheuristic_designer.strategies.local_search

"""
Local Search strategy (single solution, multiple perturbations per iteration).
"""

from __future__ import annotations
from typing import Optional
from ..initializer import Initializer
from ..search_strategy import SearchStrategy
from ..operator import Operator
from ..population import Population
from ..survivor_selection_base import SurvivorSelection
from ..survivor_selection import create_survivor_selection
from ..utils import RNGLike




class LocalSearch(SearchStrategy):
    """
    Local Search algorithm.

    At each iteration the current solution is duplicated *iterations*
    times, and every copy is perturbed independently.  The best
    among the original and the perturbed copies survives.  By default,
    the survivor selection is set to ``"local_search"`` (one parent
    vs. many offspring).

    Parameters
    ----------
    initializer : Initializer
        Population initializer.
    operator : Operator, optional
        Perturbation operator.
    survivor_sel : SurvivorSelection, optional
        Survivor selection; defaults to ``"local_search"``.
    name : str, optional
        Display name (default ``"LocalSearch"``).
    iterations : int, optional
        Number of perturbed copies per iteration (default 100).
    random_state : RNGLike, optional
        Random number generator.
    **kwargs
        Forwarded to :class:`SearchStrategy`.
    """

    def __init__(
        self,
        initializer: Initializer,
        operator: Optional[Operator] = None,
        survivor_sel: Optional[SurvivorSelection] = None,
        name: str = "LocalSearch",
        iterations: int = 100,
        random_state: Optional[RNGLike] = None,
        **kwargs,
    ):
        if survivor_sel is None:
            survivor_sel = create_survivor_selection("local_search")

        super().__init__(
            initializer,
            operator=operator,
            survivor_sel=survivor_sel,
            name=name,
            random_state=random_state,
            # Forced kwargs
            iterations=iterations,
            **kwargs,
        )



    def perturb(self, parents: Population, **kwargs) -> Population:
        """Duplicate the parent *iterations* times, then apply the operator.

        Parameters
        ----------
        parents : Population
            The current solution(s).
        **kwargs
            Forwarded to :class:`SearchStrategy.perturb`.

        Returns
        -------
        Population
            The perturbed copies.
        """

        new_population = parents.repeat(self.params.iterations)
        return super().perturb(new_population, **kwargs)