"""MOEAD sub problem algorithm class
"""
# main imports
import logging
# module imports
from ..Algorithm import Algorithm
[docs]class MOSubProblem(Algorithm):
"""Specific MO sub problem used into MOEAD
Attributes:
index: {int} -- sub problem index
weights: {[float]} -- sub problems objectives weights
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
"""
def __init__(self,
_index,
_weights,
_initalizer,
_evaluator,
_operators,
_policy,
_validator,
_maximise=True,
_parent=None):
super().__init__(_initalizer, _evaluator, _operators, _policy,
_validator, _maximise, _parent)
self.index = _index
self.weights = _weights
[docs] def run(self, _evaluations):
"""
Run the local search algorithm
Args:
_evaluations: {int} -- number of evaluations
Returns:
{Solution} -- best solution found
"""
# by default use of mother method to initialize variables
super().run(_evaluations)
# initialize solution if necessary
if self.bestSolution is None:
self.initRun()
# new operators list keep track of current sub problem
for op in self.operators:
op.setAlgo(self)
for _ in range(_evaluations):
# keep reference of sub problem used
self.policy.setAlgo(self)
# 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()
# stop algorithm if necessary
if self.stop():
break
logging.info("---- Current %s - SCORE %s" %
(newSolution, newSolution.fitness()))
logging.info("End of %s, best solution found %s" %
(type(self).__name__, self.bestSolution))
return self.bestSolution