Source code for macop.algorithms.multi.MOSubProblem

"""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 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 """ 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