``monobjective_PSO``
===========================================================

.. py:module:: optimeed.optimize.optiAlgorithms.monobjective_PSO


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   optimeed.optimize.optiAlgorithms.monobjective_PSO.MaxTimeTerminationCondition
   optimeed.optimize.optiAlgorithms.monobjective_PSO.ConvergenceTerminationCondition
   optimeed.optimize.optiAlgorithms.monobjective_PSO.Monobjective_PSO




.. py:class:: MaxTimeTerminationCondition(maxTime)


   .. py:method:: shouldTerminate()



.. py:class:: ConvergenceTerminationCondition(convergence, minrelchange_percent=0.1, nb_generation=15)


   .. py:method:: shouldTerminate()



.. py:class:: Monobjective_PSO


   Bases: :py:obj:`optimeed.optimize.optiAlgorithms.algorithmInterface.AlgorithmInterface`, :py:obj:`optimeed.core.Option_class`

   Interface for the optimization algorithm

   .. py:attribute:: NUMBER_OF_CORES
      :value: 1

      

   .. py:attribute:: KWARGS_ALGO
      :value: 2

      

   .. py:method:: initialize(initialVectorGuess, listOfOptimizationVariables)

      This function is called once parameters can't be changed anymore, before "get_convergence".

      :param initialVectorGuess: list of variables that describe the initial individual
      :param listOfOptimizationVariables: list of :class:`optimeed.optimize.optiVariable.OptimizationVariable`
      :return:


   .. py:method:: compute()

      Launch the optimization

      :return: vector of optimal variables


   .. py:method:: set_evaluationFunction(evaluationFunction, callback_on_evaluate, numberOfObjectives, _numberOfConstraints, array_evaluator)

      Set the evaluation function and all the necessary callbacks

      :param evaluationFunction: check :meth:`~optimeed.optimize.optimizer.evaluateObjectiveAndConstraints`
      :param callback_on_evaluation: check :meth:`~optimeed.optimize.optimizer.callback_on_evaluation`. Call this function after performing the evaluation of the individuals
      :param numberOfObjectives: int, number of objectives
      :param numberOfConstraints: int, number of constraints
      :param array_evaluator: If True, evaluate each generation at once using numpy array. Use it only with care, as it dismisses some features (expert mode)


   .. py:method:: set_terminationCondition(theTerminationCondition)


   .. py:method:: set_maxtime(maxTime)

      Set maximum optimization time (in seconds)


   .. py:method:: __str__()

      Return str(self).


   .. py:method:: get_convergence()

      Get the convergence of the optimization

      :return: :class:`~optimeed.optimize.optiAlgorithms.convergence.interfaceConvergence.InterfaceConvergence`