carbatpy.optimizations.opti_NSGA2
=================================

.. py:module:: carbatpy.optimizations.opti_NSGA2


Functions
---------

.. autoapisummary::

   carbatpy.optimizations.opti_NSGA2.optimize


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

.. py:function:: optimize(mode: str, config: dict | str, boundaries: dict, same_fluid: bool = True, heat_losses: float = 0.0, verbose: bool = True, n_processes: int | None = None, maxtasksperchild: int = 20, n_ieq_constr: int = 3, pop_size: int = 50, n_offspring: int | None = None, sampling_iterations: int = 50, crowding_func: str = 'pcd', eliminate_duplicates: bool = True, n_gen: int = 100, period: int = 20, ftol: float = 0.0001, save_history: bool = False, return_least_infeasible: bool = False)

   Perform a multi-objective optimization using NSGA2.

   Sets up the problem, initializes the NSGA2 algorithm, handles parallelization,
   and executes the optimization using Pymoo.

   :param mode: Optimization mode. Must currently be ``'cb'``.
   :type mode: str
   :param config: Configuration dictionary or path to configuration file.
   :type config: dict | str
   :param boundaries: Lower and upper bounds for all optimization variables.
   :type boundaries: dict
   :param same_fluid: Whether the working fluid is the same for all cycles.
                      Only used in ``'cb'`` mode. Defaults to ``True``.
   :type same_fluid: bool
   :param heat_losses: Fraction of heat losses in the system.
                       Only used in ``'cb'`` mode. Defaults to ``0.0``.
   :type heat_losses: float
   :param verbose: Whether to print progress information. Defaults to ``True``.
   :type verbose: bool
   :param n_processes: Number of parallel processes.
                       Auto-detected via :func:`default_n_processes` if ``None``.
   :type n_processes: int | None
   :param maxtasksperchild: Maximum number of tasks per worker process.
                            Defaults to ``20``.
   :type maxtasksperchild: int
   :param n_ieq_constr: Number of inequality constraints. Defaults to ``3``.
   :type n_ieq_constr: int
   :param pop_size: Population size for NSGA2. Defaults to ``50``.
   :type pop_size: int
   :param n_offspring: Number of offspring per generation.
                       Defaults to NSGA2 internal rules if ``None``.
   :type n_offspring: int | None
   :param sampling_iterations: Iterations for Latin Hypercube sampling.
                               Defaults to ``50``.
   :type sampling_iterations: int
   :param crowding_func: Crowding distance function for survival selection.
                         Defaults to ``'pcd'``.
   :type crowding_func: str
   :param eliminate_duplicates: Whether to remove duplicate solutions.
                                Defaults to ``True``.
   :type eliminate_duplicates: bool
   :param n_gen: Maximum number of generations. Defaults to ``100``.
   :type n_gen: int
   :param period: Period for robust termination checking. Defaults to ``20``.
   :type period: int
   :param ftol: Function tolerance for termination. Defaults to ``1e-4``.
   :type ftol: float
   :param save_history: Whether to save the optimization history.
                        Defaults to ``False``.
   :type save_history: bool
   :param return_least_infeasible: Whether to return the least infeasible
                                   solution if no feasible solution is found. Defaults to ``False``.
   :type return_least_infeasible: bool

   :raises ValueError: If ``boundaries`` is ``None`` or ``mode`` is not supported.

   :returns: Result object containing the Pareto front,
             best solutions, and other optimization metadata.
   :rtype: pymoo.core.result.Result