Source code for macop.algorithms.mono.IteratedLocalSearch

"""Iterated Local Search Algorithm implementation
"""

# main imports
import logging

# module imports
from ..Algorithm import Algorithm
from .LocalSearch import LocalSearch


[docs]class IteratedLocalSearch(Algorithm): """Iterated Local Search used to avoid local optima and increave EvE (Exploration vs Exploitation) compromise 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, _ls_evaluations=100): """ Run the iterated local search algorithm using local search (EvE compromise) Args: _evaluations: {int} -- number of global evaluations for ILS _ls_evaluations: {int} -- number of Local search evaluations (default: 100) Returns: {Solution} -- best solution found """ # by default use of mother method to initialize variables super().run(_evaluations) # enable resuming for ILS self.resume() # initialize current solution self.initRun() # passing global evaluation param from ILS ls = LocalSearch(self.initializer, self.evaluator, self.operators, self.policy, self.validator, self.maximise, _parent=self) # add same callbacks for callback in self.callbacks: ls.addCallback(callback) # local search algorithm implementation while not self.stop(): # create and search solution from local search newSolution = ls.run(_ls_evaluations) # if better solution than currently, replace it if self.isBetter(newSolution): self.bestSolution = newSolution # number of evaluatins increased from LocalSearch # increase number of evaluations and progress are then not necessary there #self.increaseEvaluation() #self.progress() self.information() logging.info("End of %s, best solution found %s" % (type(self).__name__, self.bestSolution)) self.end() return self.bestSolution