"""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
checkpoint: {Checkpoint} -- Checkpoint class implementation to keep track of algorithm and restart
"""
[docs] def run(self, _evaluations):
"""
Run the local search algorithm
Attributes:
_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