"""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 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, _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