# Source code for macop.algorithms.multi.MOSubProblem

    """MOEAD sub problem algorithm class
    """

    # main imports
    import logging

    # module imports
    from ..Algorithm import Algorithm

    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

        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)

            for _ in range(_evaluations):
                # 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