Thesis-NoiseDectection-rfe-attributes/optimization/algorithms/LocalSearch.py

# main imports
import logging

# module imports
from .Algorithm import Algorithm

class LocalSearch(Algorithm):

    def run(self, _evaluations):

        # 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
                # send random solution as second parameter for mutation
                newSolution = self.update(self.bestSolution, self.initializer())

                # 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