Source code for macop.algorithms.mono.HillClimberFirstImprovment

"""Hill Climber First Improvment algorithm starting from new solution and explore using neighborhood and loop over the best one obtained from neighborhood search space
"""

# main imports
import logging

# module imports
from ..Algorithm import Algorithm


[docs]class HillClimberFirstImprovment(Algorithm): """Hill Climber First Improvment used as quick exploration optimisation algorithm This algorithm do a neighborhood exploration of a new generated solution (by doing operation on the current solution obtained) in order to find a better solution from the neighborhood space. Then replace the current solution by the first one from the neighbordhood space which is better than the current solution. Do these steps until a number of evaluation (stopping criterion) is reached. Attributes: initalizer: {function} -- basic function strategy to initialize solution evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives) operators: {[Operator]} -- list of operator to use when launching algorithm policy: {Policy} -- Policy class implementation strategy to select operators validator: {function} -- basic function to check if solution is valid or not under some constraints maximise: {bool} -- specify kind of optimisation problem currentSolution: {Solution} -- current solution managed for current evaluation bestSolution: {Solution} -- best solution found so far during running algorithm callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm """
[docs] def run(self, evaluations): """ Run the local search algorithm Args: evaluations: {int} -- number of Local search evaluations Returns: {Solution} -- best solution found """ # by default use of mother method to initialize variables super().run(evaluations) # initialize current solution and best solution self.initRun() solutionSize = self._currentSolution._size # local search algorithm implementation while not self.stop(): for _ in range(solutionSize): # update current solution using policy newSolution = self.update(self._currentSolution) # if better solution than currently, replace it and stop current exploration (first improvment) if self.isBetter(newSolution): self._bestSolution = newSolution break # increase number of evaluations self.increaseEvaluation() self.progress() logging.info(f"---- Current {newSolution} - SCORE {newSolution.fitness()}") # stop algorithm if necessary if self.stop(): break # set new current solution using best solution found in this neighbor search self._currentSolution = self._bestSolution logging.info(f"End of {type(self).__name__}, best solution found {self._bestSolution}") return self._bestSolution