Source code for macop.algorithms.mono.LocalSearch

"""Local Search algorithm
"""

# main imports
import logging

# module imports
from ..Algorithm import Algorithm


[docs]class LocalSearch(Algorithm): """Local Search used as exploitation optimization algorithm 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 optimization 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) solutionSize = self.bestSolution.size # local search algorithm implementation while not self.stop(): for _ in range(solutionSize): # update solution using policy newSolution = self.update(self.bestSolution) # if better solution than currently, replace it if self.isBetter(newSolution): self.bestSolution = newSolution # increase number of evaluations self.increaseEvaluation() self.progress() logging.info("---- Current %s - SCORE %s" % (newSolution, newSolution.fitness())) # stop algorithm if necessary if self.stop(): break logging.info("End of %s, best solution found %s" % (type(self).__name__, self.bestSolution)) return self.bestSolution