Vehicle-cpp/hkpc/software/scipy/optimize/_differentialevolution.py

"""
differential_evolution: The differential evolution global optimization algorithm
Added by Andrew Nelson 2014
"""
import warnings

import numpy as np
from scipy.optimize import OptimizeResult, minimize
from scipy.optimize._optimize import _status_message
from scipy._lib._util import check_random_state, MapWrapper, _FunctionWrapper

from scipy.optimize._constraints import (Bounds, new_bounds_to_old,
                                         NonlinearConstraint, LinearConstraint)
from scipy.sparse import issparse

__all__ = ['differential_evolution']


_MACHEPS = np.finfo(np.float64).eps


def differential_evolution(func, bounds, args=(), strategy='best1bin',
                           maxiter=1000, popsize=15, tol=0.01,
                           mutation=(0.5, 1), recombination=0.7, seed=None,
                           callback=None, disp=False, polish=True,
                           init='latinhypercube', atol=0, updating='immediate',
                           workers=1, constraints=(), x0=None, *,
                           integrality=None, vectorized=False):
    """Finds the global minimum of a multivariate function.

    The differential evolution method [1]_ is stochastic in nature. It does
    not use gradient methods to find the minimum, and can search large areas
    of candidate space, but often requires larger numbers of function
    evaluations than conventional gradient-based techniques.

    The algorithm is due to Storn and Price [2]_.

    Parameters
    ----------
    func : callable
        The objective function to be minimized. Must be in the form
        ``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
        and ``args`` is a tuple of any additional fixed parameters needed to
        completely specify the function. The number of parameters, N, is equal
        to ``len(x)``.
    bounds : sequence or `Bounds`
        Bounds for variables. There are two ways to specify the bounds:
        1. Instance of `Bounds` class.
        2. ``(min, max)`` pairs for each element in ``x``, defining the finite
        lower and upper bounds for the optimizing argument of `func`.
        The total number of bounds is used to determine the number of
        parameters, N.
    args : tuple, optional
        Any additional fixed parameters needed to
        completely specify the objective function.
    strategy : str, optional
        The differential evolution strategy to use. Should be one of:

            - 'best1bin'
            - 'best1exp'
            - 'rand1exp'
            - 'randtobest1exp'
            - 'currenttobest1exp'
            - 'best2exp'
            - 'rand2exp'
            - 'randtobest1bin'
            - 'currenttobest1bin'
            - 'best2bin'
            - 'rand2bin'
            - 'rand1bin'

        The default is 'best1bin'.
    maxiter : int, optional
        The maximum number of generations over which the entire population is
        evolved. The maximum number of function evaluations (with no polishing)
        is: ``(maxiter + 1) * popsize * N``
    popsize : int, optional
        A multiplier for setting the total population size. The population has
        ``popsize * N`` individuals. This keyword is overridden if an
        initial population is supplied via the `init` keyword. When using
        ``init='sobol'`` the population size is calculated as the next power
        of 2 after ``popsize * N``.
    tol : float, optional
        Relative tolerance for convergence, the solving stops when
        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
        where and `atol` and `tol` are the absolute and relative tolerance
        respectively.
    mutation : float or tuple(float, float), optional
        The mutation constant. In the literature this is also known as
        differential weight, being denoted by F.
        If specified as a float it should be in the range [0, 2].
        If specified as a tuple ``(min, max)`` dithering is employed. Dithering
        randomly changes the mutation constant on a generation by generation
        basis. The mutation constant for that generation is taken from
        ``U[min, max)``. Dithering can help speed convergence significantly.
        Increasing the mutation constant increases the search radius, but will
        slow down convergence.
    recombination : float, optional
        The recombination constant, should be in the range [0, 1]. In the
        literature this is also known as the crossover probability, being
        denoted by CR. Increasing this value allows a larger number of mutants
        to progress into the next generation, but at the risk of population
        stability.
    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
        singleton is used.
        If `seed` is an int, a new ``RandomState`` instance is used,
        seeded with `seed`.
        If `seed` is already a ``Generator`` or ``RandomState`` instance then
        that instance is used.
        Specify `seed` for repeatable minimizations.
    disp : bool, optional
        Prints the evaluated `func` at every iteration.
    callback : callable, `callback(xk, convergence=val)`, optional
        A function to follow the progress of the minimization. ``xk`` is
        the best solution found so far. ``val`` represents the fractional
        value of the population convergence.  When ``val`` is greater than one
        the function halts. If callback returns `True`, then the minimization
        is halted (any polishing is still carried out).
    polish : bool, optional
        If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
        method is used to polish the best population member at the end, which
        can improve the minimization slightly. If a constrained problem is
        being studied then the `trust-constr` method is used instead. For large
        problems with many constraints, polishing can take a long time due to
        the Jacobian computations.
    init : str or array-like, optional
        Specify which type of population initialization is performed. Should be
        one of:

            - 'latinhypercube'
            - 'sobol'
            - 'halton'
            - 'random'
            - array specifying the initial population. The array should have
              shape ``(S, N)``, where S is the total population size and N is
              the number of parameters.
              `init` is clipped to `bounds` before use.

        The default is 'latinhypercube'. Latin Hypercube sampling tries to
        maximize coverage of the available parameter space.

        'sobol' and 'halton' are superior alternatives and maximize even more
        the parameter space. 'sobol' will enforce an initial population
        size which is calculated as the next power of 2 after
        ``popsize * N``. 'halton' has no requirements but is a bit less
        efficient. See `scipy.stats.qmc` for more details.

        'random' initializes the population randomly - this has the drawback
        that clustering can occur, preventing the whole of parameter space
        being covered. Use of an array to specify a population could be used,
        for example, to create a tight bunch of initial guesses in an location
        where the solution is known to exist, thereby reducing time for
        convergence.
    atol : float, optional
        Absolute tolerance for convergence, the solving stops when
        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
        where and `atol` and `tol` are the absolute and relative tolerance
        respectively.
    updating : {'immediate', 'deferred'}, optional
        If ``'immediate'``, the best solution vector is continuously updated
        within a single generation [4]_. This can lead to faster convergence as
        trial vectors can take advantage of continuous improvements in the best
        solution.
        With ``'deferred'``, the best solution vector is updated once per
        generation. Only ``'deferred'`` is compatible with parallelization or
        vectorization, and the `workers` and `vectorized` keywords can
        over-ride this option.

        .. versionadded:: 1.2.0

    workers : int or map-like callable, optional
        If `workers` is an int the population is subdivided into `workers`
        sections and evaluated in parallel
        (uses `multiprocessing.Pool <multiprocessing>`).
        Supply -1 to use all available CPU cores.
        Alternatively supply a map-like callable, such as
        `multiprocessing.Pool.map` for evaluating the population in parallel.
        This evaluation is carried out as ``workers(func, iterable)``.
        This option will override the `updating` keyword to
        ``updating='deferred'`` if ``workers != 1``.
        This option overrides the `vectorized` keyword if ``workers != 1``.
        Requires that `func` be pickleable.

        .. versionadded:: 1.2.0

    constraints : {NonLinearConstraint, LinearConstraint, Bounds}
        Constraints on the solver, over and above those applied by the `bounds`
        kwd. Uses the approach by Lampinen [5]_.

        .. versionadded:: 1.4.0

    x0 : None or array-like, optional
        Provides an initial guess to the minimization. Once the population has
        been initialized this vector replaces the first (best) member. This
        replacement is done even if `init` is given an initial population.
        ``x0.shape == (N,)``.

        .. versionadded:: 1.7.0

    integrality : 1-D array, optional
        For each decision variable, a boolean value indicating whether the
        decision variable is constrained to integer values. The array is
        broadcast to ``(N,)``.
        If any decision variables are constrained to be integral, they will not
        be changed during polishing.
        Only integer values lying between the lower and upper bounds are used.
        If there are no integer values lying between the bounds then a
        `ValueError` is raised.

        .. versionadded:: 1.9.0

    vectorized : bool, optional
        If ``vectorized is True``, `func` is sent an `x` array with
        ``x.shape == (N, S)``, and is expected to return an array of shape
        ``(S,)``, where `S` is the number of solution vectors to be calculated.
        If constraints are applied, each of the functions used to construct
        a `Constraint` object should accept an `x` array with
        ``x.shape == (N, S)``, and return an array of shape ``(M, S)``, where
        `M` is the number of constraint components.
        This option is an alternative to the parallelization offered by
        `workers`, and may help in optimization speed by reducing interpreter
        overhead from multiple function calls. This keyword is ignored if
        ``workers != 1``.
        This option will override the `updating` keyword to
        ``updating='deferred'``.
        See the notes section for further discussion on when to use
        ``'vectorized'``, and when to use ``'workers'``.

        .. versionadded:: 1.9.0

    Returns
    -------
    res : OptimizeResult
        The optimization result represented as a `OptimizeResult` object.
        Important attributes are: ``x`` the solution array, ``success`` a
        Boolean flag indicating if the optimizer exited successfully and
        ``message`` which describes the cause of the termination. See
        `OptimizeResult` for a description of other attributes. If `polish`
        was employed, and a lower minimum was obtained by the polishing, then
        OptimizeResult also contains the ``jac`` attribute.
        If the eventual solution does not satisfy the applied constraints
        ``success`` will be `False`.

    Notes
    -----
    Differential evolution is a stochastic population based method that is
    useful for global optimization problems. At each pass through the
    population the algorithm mutates each candidate solution by mixing with
    other candidate solutions to create a trial candidate. There are several
    strategies [3]_ for creating trial candidates, which suit some problems
    more than others. The 'best1bin' strategy is a good starting point for
    many systems. In this strategy two members of the population are randomly
    chosen. Their difference is used to mutate the best member (the 'best' in
    'best1bin'), :math:`b_0`, so far:

    .. math::

        b' = b_0 + mutation * (population[rand0] - population[rand1])

    A trial vector is then constructed. Starting with a randomly chosen ith
    parameter the trial is sequentially filled (in modulo) with parameters
    from ``b'`` or the original candidate. The choice of whether to use ``b'``
    or the original candidate is made with a binomial distribution (the 'bin'
    in 'best1bin') - a random number in [0, 1) is generated. If this number is
    less than the `recombination` constant then the parameter is loaded from
    ``b'``, otherwise it is loaded from the original candidate. The final
    parameter is always loaded from ``b'``. Once the trial candidate is built
    its fitness is assessed. If the trial is better than the original candidate
    then it takes its place. If it is also better than the best overall
    candidate it also replaces that.
    To improve your chances of finding a global minimum use higher `popsize`
    values, with higher `mutation` and (dithering), but lower `recombination`
    values. This has the effect of widening the search radius, but slowing
    convergence.
    By default the best solution vector is updated continuously within a single
    iteration (``updating='immediate'``). This is a modification [4]_ of the
    original differential evolution algorithm which can lead to faster
    convergence as trial vectors can immediately benefit from improved
    solutions. To use the original Storn and Price behaviour, updating the best
    solution once per iteration, set ``updating='deferred'``.
    The ``'deferred'`` approach is compatible with both parallelization and
    vectorization (``'workers'`` and ``'vectorized'`` keywords). These may
    improve minimization speed by using computer resources more efficiently.
    The ``'workers'`` distribute calculations over multiple processors. By
    default the Python `multiprocessing` module is used, but other approaches
    are also possible, such as the Message Passing Interface (MPI) used on
    clusters [6]_ [7]_. The overhead from these approaches (creating new
    Processes, etc) may be significant, meaning that computational speed
    doesn't necessarily scale with the number of processors used.
    Parallelization is best suited to computationally expensive objective
    functions. If the objective function is less expensive, then
    ``'vectorized'`` may aid by only calling the objective function once per
    iteration, rather than multiple times for all the population members; the
    interpreter overhead is reduced.

    .. versionadded:: 0.15.0

    References
    ----------
    .. [1] Differential evolution, Wikipedia,
           http://en.wikipedia.org/wiki/Differential_evolution
    .. [2] Storn, R and Price, K, Differential Evolution - a Simple and
           Efficient Heuristic for Global Optimization over Continuous Spaces,
           Journal of Global Optimization, 1997, 11, 341 - 359.
    .. [3] http://www1.icsi.berkeley.edu/~storn/code.html
    .. [4] Wormington, M., Panaccione, C., Matney, K. M., Bowen, D. K., -
           Characterization of structures from X-ray scattering data using
           genetic algorithms, Phil. Trans. R. Soc. Lond. A, 1999, 357,
           2827-2848
    .. [5] Lampinen, J., A constraint handling approach for the differential
           evolution algorithm. Proceedings of the 2002 Congress on
           Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). Vol. 2. IEEE,
           2002.
    .. [6] https://mpi4py.readthedocs.io/en/stable/
    .. [7] https://schwimmbad.readthedocs.io/en/latest/

    Examples
    --------
    Let us consider the problem of minimizing the Rosenbrock function. This
    function is implemented in `rosen` in `scipy.optimize`.

    >>> import numpy as np
    >>> from scipy.optimize import rosen, differential_evolution
    >>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
    >>> result = differential_evolution(rosen, bounds)
    >>> result.x, result.fun
    (array([1., 1., 1., 1., 1.]), 1.9216496320061384e-19)

    Now repeat, but with parallelization.

    >>> result = differential_evolution(rosen, bounds, updating='deferred',
    ...                                 workers=2)
    >>> result.x, result.fun
    (array([1., 1., 1., 1., 1.]), 1.9216496320061384e-19)

    Let's do a constrained minimization.

    >>> from scipy.optimize import LinearConstraint, Bounds

    We add the constraint that the sum of ``x[0]`` and ``x[1]`` must be less
    than or equal to 1.9.  This is a linear constraint, which may be written
    ``A @ x <= 1.9``, where ``A = array([[1, 1]])``.  This can be encoded as
    a `LinearConstraint` instance:

    >>> lc = LinearConstraint([[1, 1]], -np.inf, 1.9)

    Specify limits using a `Bounds` object.

    >>> bounds = Bounds([0., 0.], [2., 2.])
    >>> result = differential_evolution(rosen, bounds, constraints=lc,
    ...                                 seed=1)
    >>> result.x, result.fun
    (array([0.96632622, 0.93367155]), 0.0011352416852625719)

    Next find the minimum of the Ackley function
    (https://en.wikipedia.org/wiki/Test_functions_for_optimization).

    >>> def ackley(x):
    ...     arg1 = -0.2 * np.sqrt(0.5 * (x[0] ** 2 + x[1] ** 2))
    ...     arg2 = 0.5 * (np.cos(2. * np.pi * x[0]) + np.cos(2. * np.pi * x[1]))
    ...     return -20. * np.exp(arg1) - np.exp(arg2) + 20. + np.e
    >>> bounds = [(-5, 5), (-5, 5)]
    >>> result = differential_evolution(ackley, bounds, seed=1)
    >>> result.x, result.fun
    (array([0., 0.]), 4.440892098500626e-16)

    The Ackley function is written in a vectorized manner, so the
    ``'vectorized'`` keyword can be employed. Note the reduced number of
    function evaluations.

    >>> result = differential_evolution(
    ...     ackley, bounds, vectorized=True, updating='deferred', seed=1
    ... )
    >>> result.x, result.fun
    (array([0., 0.]), 4.440892098500626e-16)

    """

    # using a context manager means that any created Pool objects are
    # cleared up.
    with DifferentialEvolutionSolver(func, bounds, args=args,
                                     strategy=strategy,
                                     maxiter=maxiter,
                                     popsize=popsize, tol=tol,
                                     mutation=mutation,
                                     recombination=recombination,
                                     seed=seed, polish=polish,
                                     callback=callback,
                                     disp=disp, init=init, atol=atol,
                                     updating=updating,
                                     workers=workers,
                                     constraints=constraints,
                                     x0=x0,
                                     integrality=integrality,
                                     vectorized=vectorized) as solver:
        ret = solver.solve()

    return ret


class DifferentialEvolutionSolver:

    """This class implements the differential evolution solver

    Parameters
    ----------
    func : callable
        The objective function to be minimized. Must be in the form
        ``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
        and ``args`` is a tuple of any additional fixed parameters needed to
        completely specify the function. The number of parameters, N, is equal
        to ``len(x)``.
    bounds : sequence or `Bounds`
        Bounds for variables. There are two ways to specify the bounds:
        1. Instance of `Bounds` class.
        2. ``(min, max)`` pairs for each element in ``x``, defining the finite
        lower and upper bounds for the optimizing argument of `func`.
        The total number of bounds is used to determine the number of
        parameters, N.
    args : tuple, optional
        Any additional fixed parameters needed to
        completely specify the objective function.
    strategy : str, optional
        The differential evolution strategy to use. Should be one of:

            - 'best1bin'
            - 'best1exp'
            - 'rand1exp'
            - 'randtobest1exp'
            - 'currenttobest1exp'
            - 'best2exp'
            - 'rand2exp'
            - 'randtobest1bin'
            - 'currenttobest1bin'
            - 'best2bin'
            - 'rand2bin'
            - 'rand1bin'

        The default is 'best1bin'

    maxiter : int, optional
        The maximum number of generations over which the entire population is
        evolved. The maximum number of function evaluations (with no polishing)
        is: ``(maxiter + 1) * popsize * N``
    popsize : int, optional
        A multiplier for setting the total population size. The population has
        ``popsize * N`` individuals. This keyword is overridden if an
        initial population is supplied via the `init` keyword. When using
        ``init='sobol'`` the population size is calculated as the next power
        of 2 after ``popsize * N``.
    tol : float, optional
        Relative tolerance for convergence, the solving stops when
        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
        where and `atol` and `tol` are the absolute and relative tolerance
        respectively.
    mutation : float or tuple(float, float), optional
        The mutation constant. In the literature this is also known as
        differential weight, being denoted by F.
        If specified as a float it should be in the range [0, 2].
        If specified as a tuple ``(min, max)`` dithering is employed. Dithering
        randomly changes the mutation constant on a generation by generation
        basis. The mutation constant for that generation is taken from
        U[min, max). Dithering can help speed convergence significantly.
        Increasing the mutation constant increases the search radius, but will
        slow down convergence.
    recombination : float, optional
        The recombination constant, should be in the range [0, 1]. In the
        literature this is also known as the crossover probability, being
        denoted by CR. Increasing this value allows a larger number of mutants
        to progress into the next generation, but at the risk of population
        stability.
    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
        singleton is used.
        If `seed` is an int, a new ``RandomState`` instance is used,
        seeded with `seed`.
        If `seed` is already a ``Generator`` or ``RandomState`` instance then
        that instance is used.
        Specify `seed` for repeatable minimizations.
    disp : bool, optional
        Prints the evaluated `func` at every iteration.
    callback : callable, `callback(xk, convergence=val)`, optional
        A function to follow the progress of the minimization. ``xk`` is
        the current value of ``x0``. ``val`` represents the fractional
        value of the population convergence. When ``val`` is greater than one
        the function halts. If callback returns `True`, then the minimization
        is halted (any polishing is still carried out).
    polish : bool, optional
        If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
        method is used to polish the best population member at the end, which
        can improve the minimization slightly. If a constrained problem is
        being studied then the `trust-constr` method is used instead. For large
        problems with many constraints, polishing can take a long time due to
        the Jacobian computations.
    maxfun : int, optional
        Set the maximum number of function evaluations. However, it probably
        makes more sense to set `maxiter` instead.
    init : str or array-like, optional
        Specify which type of population initialization is performed. Should be
        one of:

            - 'latinhypercube'
            - 'sobol'
            - 'halton'
            - 'random'
            - array specifying the initial population. The array should have
              shape ``(S, N)``, where S is the total population size and
              N is the number of parameters.
              `init` is clipped to `bounds` before use.

        The default is 'latinhypercube'. Latin Hypercube sampling tries to
        maximize coverage of the available parameter space.

        'sobol' and 'halton' are superior alternatives and maximize even more
        the parameter space. 'sobol' will enforce an initial population
        size which is calculated as the next power of 2 after
        ``popsize * N``. 'halton' has no requirements but is a bit less
        efficient. See `scipy.stats.qmc` for more details.

        'random' initializes the population randomly - this has the drawback
        that clustering can occur, preventing the whole of parameter space
        being covered. Use of an array to specify a population could be used,
        for example, to create a tight bunch of initial guesses in an location
        where the solution is known to exist, thereby reducing time for
        convergence.
    atol : float, optional
        Absolute tolerance for convergence, the solving stops when
        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
        where and `atol` and `tol` are the absolute and relative tolerance
        respectively.
    updating : {'immediate', 'deferred'}, optional
        If ``'immediate'``, the best solution vector is continuously updated
        within a single generation [4]_. This can lead to faster convergence as
        trial vectors can take advantage of continuous improvements in the best
        solution.
        With ``'deferred'``, the best solution vector is updated once per
        generation. Only ``'deferred'`` is compatible with parallelization or
        vectorization, and the `workers` and `vectorized` keywords can
        over-ride this option.
    workers : int or map-like callable, optional
        If `workers` is an int the population is subdivided into `workers`
        sections and evaluated in parallel
        (uses `multiprocessing.Pool <multiprocessing>`).
        Supply `-1` to use all cores available to the Process.
        Alternatively supply a map-like callable, such as
        `multiprocessing.Pool.map` for evaluating the population in parallel.
        This evaluation is carried out as ``workers(func, iterable)``.
        This option will override the `updating` keyword to
        `updating='deferred'` if `workers != 1`.
        Requires that `func` be pickleable.
    constraints : {NonLinearConstraint, LinearConstraint, Bounds}
        Constraints on the solver, over and above those applied by the `bounds`
        kwd. Uses the approach by Lampinen.
    x0 : None or array-like, optional
        Provides an initial guess to the minimization. Once the population has
        been initialized this vector replaces the first (best) member. This
        replacement is done even if `init` is given an initial population.
        ``x0.shape == (N,)``.
    integrality : 1-D array, optional
        For each decision variable, a boolean value indicating whether the
        decision variable is constrained to integer values. The array is
        broadcast to ``(N,)``.
        If any decision variables are constrained to be integral, they will not
        be changed during polishing.
        Only integer values lying between the lower and upper bounds are used.
        If there are no integer values lying between the bounds then a
        `ValueError` is raised.
    vectorized : bool, optional
        If ``vectorized is True``, `func` is sent an `x` array with
        ``x.shape == (N, S)``, and is expected to return an array of shape
        ``(S,)``, where `S` is the number of solution vectors to be calculated.
        If constraints are applied, each of the functions used to construct
        a `Constraint` object should accept an `x` array with
        ``x.shape == (N, S)``, and return an array of shape ``(M, S)``, where
        `M` is the number of constraint components.
        This option is an alternative to the parallelization offered by
        `workers`, and may help in optimization speed. This keyword is
        ignored if ``workers != 1``.
        This option will override the `updating` keyword to
        ``updating='deferred'``.
    """

    # Dispatch of mutation strategy method (binomial or exponential).
    _binomial = {'best1bin': '_best1',
                 'randtobest1bin': '_randtobest1',
                 'currenttobest1bin': '_currenttobest1',
                 'best2bin': '_best2',
                 'rand2bin': '_rand2',
                 'rand1bin': '_rand1'}
    _exponential = {'best1exp': '_best1',
                    'rand1exp': '_rand1',
                    'randtobest1exp': '_randtobest1',
                    'currenttobest1exp': '_currenttobest1',
                    'best2exp': '_best2',
                    'rand2exp': '_rand2'}

    __init_error_msg = ("The population initialization method must be one of "
                        "'latinhypercube' or 'random', or an array of shape "
                        "(S, N) where N is the number of parameters and S>5")

    def __init__(self, func, bounds, args=(),
                 strategy='best1bin', maxiter=1000, popsize=15,
                 tol=0.01, mutation=(0.5, 1), recombination=0.7, seed=None,
                 maxfun=np.inf, callback=None, disp=False, polish=True,
                 init='latinhypercube', atol=0, updating='immediate',
                 workers=1, constraints=(), x0=None, *, integrality=None,
                 vectorized=False):

        if strategy in self._binomial:
            self.mutation_func = getattr(self, self._binomial[strategy])
        elif strategy in self._exponential:
            self.mutation_func = getattr(self, self._exponential[strategy])
        else:
            raise ValueError("Please select a valid mutation strategy")
        self.strategy = strategy

        self.callback = callback
        self.polish = polish

        # set the updating / parallelisation options
        if updating in ['immediate', 'deferred']:
            self._updating = updating

        self.vectorized = vectorized

        # want to use parallelisation, but updating is immediate
        if workers != 1 and updating == 'immediate':
            warnings.warn("differential_evolution: the 'workers' keyword has"
                          " overridden updating='immediate' to"
                          " updating='deferred'", UserWarning, stacklevel=2)
            self._updating = 'deferred'

        if vectorized and workers != 1:
            warnings.warn("differential_evolution: the 'workers' keyword"
                          " overrides the 'vectorized' keyword", stacklevel=2)
            self.vectorized = vectorized = False

        if vectorized and updating == 'immediate':
            warnings.warn("differential_evolution: the 'vectorized' keyword"
                          " has overridden updating='immediate' to updating"
                          "='deferred'", UserWarning, stacklevel=2)
            self._updating = 'deferred'

        # an object with a map method.
        if vectorized:
            def maplike_for_vectorized_func(func, x):
                # send an array (N, S) to the user func,
                # expect to receive (S,). Transposition is required because
                # internally the population is held as (S, N)
                return np.atleast_1d(func(x.T))
            workers = maplike_for_vectorized_func

        self._mapwrapper = MapWrapper(workers)

        # relative and absolute tolerances for convergence
        self.tol, self.atol = tol, atol

        # Mutation constant should be in [0, 2). If specified as a sequence
        # then dithering is performed.
        self.scale = mutation
        if (not np.all(np.isfinite(mutation)) or
                np.any(np.array(mutation) >= 2) or
                np.any(np.array(mutation) < 0)):
            raise ValueError('The mutation constant must be a float in '
                             'U[0, 2), or specified as a tuple(min, max)'
                             ' where min < max and min, max are in U[0, 2).')

        self.dither = None
        if hasattr(mutation, '__iter__') and len(mutation) > 1:
            self.dither = [mutation[0], mutation[1]]
            self.dither.sort()

        self.cross_over_probability = recombination

        # we create a wrapped function to allow the use of map (and Pool.map
        # in the future)
        self.func = _FunctionWrapper(func, args)
        self.args = args

        # convert tuple of lower and upper bounds to limits
        # [(low_0, high_0), ..., (low_n, high_n]
        #     -> [[low_0, ..., low_n], [high_0, ..., high_n]]
        if isinstance(bounds, Bounds):
            self.limits = np.array(new_bounds_to_old(bounds.lb,
                                                     bounds.ub,
                                                     len(bounds.lb)),
                                   dtype=float).T
        else:
            self.limits = np.array(bounds, dtype='float').T

        if (np.size(self.limits, 0) != 2 or not
                np.all(np.isfinite(self.limits))):
            raise ValueError('bounds should be a sequence containing '
                             'real valued (min, max) pairs for each value'
                             ' in x')

        if maxiter is None:  # the default used to be None
            maxiter = 1000
        self.maxiter = maxiter
        if maxfun is None:  # the default used to be None
            maxfun = np.inf
        self.maxfun = maxfun

        # population is scaled to between [0, 1].
        # We have to scale between parameter <-> population
        # save these arguments for _scale_parameter and
        # _unscale_parameter. This is an optimization
        self.__scale_arg1 = 0.5 * (self.limits[0] + self.limits[1])
        self.__scale_arg2 = np.fabs(self.limits[0] - self.limits[1])

        self.parameter_count = np.size(self.limits, 1)

        self.random_number_generator = check_random_state(seed)

        # Which parameters are going to be integers?
        if np.any(integrality):
            # # user has provided a truth value for integer constraints
            integrality = np.broadcast_to(
                integrality,
                self.parameter_count
            )
            integrality = np.asarray(integrality, bool)
            # For integrality parameters change the limits to only allow
            # integer values lying between the limits.
            lb, ub = np.copy(self.limits)

            lb = np.ceil(lb)
            ub = np.floor(ub)
            if not (lb[integrality] <= ub[integrality]).all():
                # there's a parameter that doesn't have an integer value
                # lying between the limits
                raise ValueError("One of the integrality constraints does not"
                                 " have any possible integer values between"
                                 " the lower/upper bounds.")
            nlb = np.nextafter(lb[integrality] - 0.5, np.inf)
            nub = np.nextafter(ub[integrality] + 0.5, -np.inf)

            self.integrality = integrality
            self.limits[0, self.integrality] = nlb
            self.limits[1, self.integrality] = nub
        else:
            self.integrality = False

        # default population initialization is a latin hypercube design, but
        # there are other population initializations possible.
        # the minimum is 5 because 'best2bin' requires a population that's at
        # least 5 long
        self.num_population_members = max(5, popsize * self.parameter_count)
        self.population_shape = (self.num_population_members,
                                 self.parameter_count)

        self._nfev = 0
        # check first str otherwise will fail to compare str with array
        if isinstance(init, str):
            if init == 'latinhypercube':
                self.init_population_lhs()
            elif init == 'sobol':
                # must be Ns = 2**m for Sobol'
                n_s = int(2 ** np.ceil(np.log2(self.num_population_members)))
                self.num_population_members = n_s
                self.population_shape = (self.num_population_members,
                                         self.parameter_count)
                self.init_population_qmc(qmc_engine='sobol')
            elif init == 'halton':
                self.init_population_qmc(qmc_engine='halton')
            elif init == 'random':
                self.init_population_random()
            else:
                raise ValueError(self.__init_error_msg)
        else:
            self.init_population_array(init)

        if x0 is not None:
            # scale to within unit interval and
            # ensure parameters are within bounds.
            x0_scaled = self._unscale_parameters(np.asarray(x0))
            if ((x0_scaled > 1.0) | (x0_scaled < 0.0)).any():
                raise ValueError(
                    "Some entries in x0 lay outside the specified bounds"
                )
            self.population[0] = x0_scaled

        # infrastructure for constraints
        self.constraints = constraints
        self._wrapped_constraints = []

        if hasattr(constraints, '__len__'):
            # sequence of constraints, this will also deal with default
            # keyword parameter
            for c in constraints:
                self._wrapped_constraints.append(
                    _ConstraintWrapper(c, self.x)
                )
        else:
            self._wrapped_constraints = [
                _ConstraintWrapper(constraints, self.x)
            ]
        self.total_constraints = np.sum(
            [c.num_constr for c in self._wrapped_constraints]
        )
        self.constraint_violation = np.zeros((self.num_population_members, 1))
        self.feasible = np.ones(self.num_population_members, bool)

        self.disp = disp

    def init_population_lhs(self):
        """
        Initializes the population with Latin Hypercube Sampling.
        Latin Hypercube Sampling ensures that each parameter is uniformly
        sampled over its range.
        """
        rng = self.random_number_generator

        # Each parameter range needs to be sampled uniformly. The scaled
        # parameter range ([0, 1)) needs to be split into
        # `self.num_population_members` segments, each of which has the following
        # size:
        segsize = 1.0 / self.num_population_members

        # Within each segment we sample from a uniform random distribution.
        # We need to do this sampling for each parameter.
        samples = (segsize * rng.uniform(size=self.population_shape)

        # Offset each segment to cover the entire parameter range [0, 1)
                   + np.linspace(0., 1., self.num_population_members,
                                 endpoint=False)[:, np.newaxis])

        # Create an array for population of candidate solutions.
        self.population = np.zeros_like(samples)

        # Initialize population of candidate solutions by permutation of the
        # random samples.
        for j in range(self.parameter_count):
            order = rng.permutation(range(self.num_population_members))
            self.population[:, j] = samples[order, j]

        # reset population energies
        self.population_energies = np.full(self.num_population_members,
                                           np.inf)

        # reset number of function evaluations counter
        self._nfev = 0

    def init_population_qmc(self, qmc_engine):
        """Initializes the population with a QMC method.

        QMC methods ensures that each parameter is uniformly
        sampled over its range.

        Parameters
        ----------
        qmc_engine : str
            The QMC method to use for initialization. Can be one of
            ``latinhypercube``, ``sobol`` or ``halton``.

        """
        from scipy.stats import qmc

        rng = self.random_number_generator

        # Create an array for population of candidate solutions.
        if qmc_engine == 'latinhypercube':
            sampler = qmc.LatinHypercube(d=self.parameter_count, seed=rng)
        elif qmc_engine == 'sobol':
            sampler = qmc.Sobol(d=self.parameter_count, seed=rng)
        elif qmc_engine == 'halton':
            sampler = qmc.Halton(d=self.parameter_count, seed=rng)
        else:
            raise ValueError(self.__init_error_msg)

        self.population = sampler.random(n=self.num_population_members)

        # reset population energies
        self.population_energies = np.full(self.num_population_members,
                                           np.inf)

        # reset number of function evaluations counter
        self._nfev = 0

    def init_population_random(self):
        """
        Initializes the population at random. This type of initialization
        can possess clustering, Latin Hypercube sampling is generally better.
        """
        rng = self.random_number_generator
        self.population = rng.uniform(size=self.population_shape)

        # reset population energies
        self.population_energies = np.full(self.num_population_members,
                                           np.inf)

        # reset number of function evaluations counter
        self._nfev = 0

    def init_population_array(self, init):
        """
        Initializes the population with a user specified population.

        Parameters
        ----------
        init : np.ndarray
            Array specifying subset of the initial population. The array should
            have shape (S, N), where N is the number of parameters.
            The population is clipped to the lower and upper bounds.
        """
        # make sure you're using a float array
        popn = np.asfarray(init)

        if (np.size(popn, 0) < 5 or
                popn.shape[1] != self.parameter_count or
                len(popn.shape) != 2):
            raise ValueError("The population supplied needs to have shape"
                             " (S, len(x)), where S > 4.")

        # scale values and clip to bounds, assigning to population
        self.population = np.clip(self._unscale_parameters(popn), 0, 1)

        self.num_population_members = np.size(self.population, 0)

        self.population_shape = (self.num_population_members,
                                 self.parameter_count)

        # reset population energies
        self.population_energies = np.full(self.num_population_members,
                                           np.inf)

        # reset number of function evaluations counter
        self._nfev = 0

    @property
    def x(self):
        """
        The best solution from the solver
        """
        return self._scale_parameters(self.population[0])

    @property
    def convergence(self):
        """
        The standard deviation of the population energies divided by their
        mean.
        """
        if np.any(np.isinf(self.population_energies)):
            return np.inf
        return (np.std(self.population_energies) /
                np.abs(np.mean(self.population_energies) + _MACHEPS))

    def converged(self):
        """
        Return True if the solver has converged.
        """
        if np.any(np.isinf(self.population_energies)):
            return False

        return (np.std(self.population_energies) <=
                self.atol +
                self.tol * np.abs(np.mean(self.population_energies)))

    def solve(self):
        """
        Runs the DifferentialEvolutionSolver.

        Returns
        -------
        res : OptimizeResult
            The optimization result represented as a ``OptimizeResult`` object.
            Important attributes are: ``x`` the solution array, ``success`` a
            Boolean flag indicating if the optimizer exited successfully and
            ``message`` which describes the cause of the termination. See
            `OptimizeResult` for a description of other attributes.  If `polish`
            was employed, and a lower minimum was obtained by the polishing,
            then OptimizeResult also contains the ``jac`` attribute.
        """
        nit, warning_flag = 0, False
        status_message = _status_message['success']

        # The population may have just been initialized (all entries are
        # np.inf). If it has you have to calculate the initial energies.
        # Although this is also done in the evolve generator it's possible
        # that someone can set maxiter=0, at which point we still want the
        # initial energies to be calculated (the following loop isn't run).
        if np.all(np.isinf(self.population_energies)):
            self.feasible, self.constraint_violation = (
                self._calculate_population_feasibilities(self.population))

            # only work out population energies for feasible solutions
            self.population_energies[self.feasible] = (
                self._calculate_population_energies(
                    self.population[self.feasible]))

            self._promote_lowest_energy()

        # do the optimization.
        for nit in range(1, self.maxiter + 1):
            # evolve the population by a generation
            try:
                next(self)
            except StopIteration:
                warning_flag = True
                if self._nfev > self.maxfun:
                    status_message = _status_message['maxfev']
                elif self._nfev == self.maxfun:
                    status_message = ('Maximum number of function evaluations'
                                      ' has been reached.')
                break

            if self.disp:
                print("differential_evolution step %d: f(x)= %g"
                      % (nit,
                         self.population_energies[0]))

            if self.callback:
                c = self.tol / (self.convergence + _MACHEPS)
                warning_flag = bool(self.callback(self.x, convergence=c))
                if warning_flag:
                    status_message = ('callback function requested stop early'
                                      ' by returning True')

            # should the solver terminate?
            if warning_flag or self.converged():
                break

        else:
            status_message = _status_message['maxiter']
            warning_flag = True

        DE_result = OptimizeResult(
            x=self.x,
            fun=self.population_energies[0],
            nfev=self._nfev,
            nit=nit,
            message=status_message,
            success=(warning_flag is not True))

        if self.polish and not np.all(self.integrality):
            # can't polish if all the parameters are integers
            if np.any(self.integrality):
                # set the lower/upper bounds equal so that any integrality
                # constraints work.
                limits, integrality = self.limits, self.integrality
                limits[0, integrality] = DE_result.x[integrality]
                limits[1, integrality] = DE_result.x[integrality]

            polish_method = 'L-BFGS-B'

            if self._wrapped_constraints:
                polish_method = 'trust-constr'

                constr_violation = self._constraint_violation_fn(DE_result.x)
                if np.any(constr_violation > 0.):
                    warnings.warn("differential evolution didn't find a"
                                  " solution satisfying the constraints,"
                                  " attempting to polish from the least"
                                  " infeasible solution", UserWarning)
            if self.disp:
                print(f"Polishing solution with '{polish_method}'")
            result = minimize(self.func,
                              np.copy(DE_result.x),
                              method=polish_method,
                              bounds=self.limits.T,
                              constraints=self.constraints)

            self._nfev += result.nfev
            DE_result.nfev = self._nfev

            # Polishing solution is only accepted if there is an improvement in
            # cost function, the polishing was successful and the solution lies
            # within the bounds.
            if (result.fun < DE_result.fun and
                    result.success and
                    np.all(result.x <= self.limits[1]) and
                    np.all(self.limits[0] <= result.x)):
                DE_result.fun = result.fun
                DE_result.x = result.x
                DE_result.jac = result.jac
                # to keep internal state consistent
                self.population_energies[0] = result.fun
                self.population[0] = self._unscale_parameters(result.x)

        if self._wrapped_constraints:
            DE_result.constr = [c.violation(DE_result.x) for
                                c in self._wrapped_constraints]
            DE_result.constr_violation = np.max(
                np.concatenate(DE_result.constr))
            DE_result.maxcv = DE_result.constr_violation
            if DE_result.maxcv > 0:
                # if the result is infeasible then success must be False
                DE_result.success = False
                DE_result.message = ("The solution does not satisfy the "
                                     f"constraints, MAXCV = {DE_result.maxcv}")

        return DE_result

    def _calculate_population_energies(self, population):
        """
        Calculate the energies of a population.

        Parameters
        ----------
        population : ndarray
            An array of parameter vectors normalised to [0, 1] using lower
            and upper limits. Has shape ``(np.size(population, 0), N)``.

        Returns
        -------
        energies : ndarray
            An array of energies corresponding to each population member. If
            maxfun will be exceeded during this call, then the number of
            function evaluations will be reduced and energies will be
            right-padded with np.inf. Has shape ``(np.size(population, 0),)``
        """
        num_members = np.size(population, 0)
        # S is the number of function evals left to stay under the
        # maxfun budget
        S = min(num_members, self.maxfun - self._nfev)

        energies = np.full(num_members, np.inf)

        parameters_pop = self._scale_parameters(population)
        try:
            calc_energies = list(
                self._mapwrapper(self.func, parameters_pop[0:S])
            )
            calc_energies = np.squeeze(calc_energies)
        except (TypeError, ValueError) as e:
            # wrong number of arguments for _mapwrapper
            # or wrong length returned from the mapper
            raise RuntimeError(
                "The map-like callable must be of the form f(func, iterable), "
                "returning a sequence of numbers the same length as 'iterable'"
            ) from e

        if calc_energies.size != S:
            if self.vectorized:
                raise RuntimeError("The vectorized function must return an"
                                   " array of shape (S,) when given an array"
                                   " of shape (len(x), S)")
            raise RuntimeError("func(x, *args) must return a scalar value")

        energies[0:S] = calc_energies

        if self.vectorized:
            self._nfev += 1
        else:
            self._nfev += S

        return energies

    def _promote_lowest_energy(self):
        # swaps 'best solution' into first population entry

        idx = np.arange(self.num_population_members)
        feasible_solutions = idx[self.feasible]
        if feasible_solutions.size:
            # find the best feasible solution
            idx_t = np.argmin(self.population_energies[feasible_solutions])
            l = feasible_solutions[idx_t]
        else:
            # no solution was feasible, use 'best' infeasible solution, which
            # will violate constraints the least
            l = np.argmin(np.sum(self.constraint_violation, axis=1))

        self.population_energies[[0, l]] = self.population_energies[[l, 0]]
        self.population[[0, l], :] = self.population[[l, 0], :]
        self.feasible[[0, l]] = self.feasible[[l, 0]]
        self.constraint_violation[[0, l], :] = (
        self.constraint_violation[[l, 0], :])

    def _constraint_violation_fn(self, x):
        """
        Calculates total constraint violation for all the constraints, for a
        set of solutions.

        Parameters
        ----------
        x : ndarray
            Solution vector(s). Has shape (S, N), or (N,), where S is the
            number of solutions to investigate and N is the number of
            parameters.

        Returns
        -------
        cv : ndarray
            Total violation of constraints. Has shape ``(S, M)``, where M is
            the total number of constraint components (which is not necessarily
            equal to len(self._wrapped_constraints)).
        """
        # how many solution vectors you're calculating constraint violations
        # for
        S = np.size(x) // self.parameter_count
        _out = np.zeros((S, self.total_constraints))
        offset = 0
        for con in self._wrapped_constraints:
            # the input/output of the (vectorized) constraint function is
            # {(N, S), (N,)} --> (M, S)
            # The input to _constraint_violation_fn is (S, N) or (N,), so
            # transpose to pass it to the constraint. The output is transposed
            # from (M, S) to (S, M) for further use.
            c = con.violation(x.T).T

            # The shape of c should be (M,), (1, M), or (S, M). Check for
            # those shapes, as an incorrect shape indicates that the
            # user constraint function didn't return the right thing, and
            # the reshape operation will fail. Intercept the wrong shape
            # to give a reasonable error message. I'm not sure what failure
            # modes an inventive user will come up with.
            if c.shape[-1] != con.num_constr or (S > 1 and c.shape[0] != S):
                raise RuntimeError("An array returned from a Constraint has"
                                   " the wrong shape. If `vectorized is False`"
                                   " the Constraint should return an array of"
                                   " shape (M,). If `vectorized is True` then"
                                   " the Constraint must return an array of"
                                   " shape (M, S), where S is the number of"
                                   " solution vectors and M is the number of"
                                   " constraint components in a given"
                                   " Constraint object.")

            # the violation function may return a 1D array, but is it a
            # sequence of constraints for one solution (S=1, M>=1), or the
            # value of a single constraint for a sequence of solutions
            # (S>=1, M=1)
            c = np.reshape(c, (S, con.num_constr))
            _out[:, offset:offset + con.num_constr] = c
            offset += con.num_constr

        return _out

    def _calculate_population_feasibilities(self, population):
        """
        Calculate the feasibilities of a population.

        Parameters
        ----------
        population : ndarray
            An array of parameter vectors normalised to [0, 1] using lower
            and upper limits. Has shape ``(np.size(population, 0), N)``.

        Returns
        -------
        feasible, constraint_violation : ndarray, ndarray
            Boolean array of feasibility for each population member, and an
            array of the constraint violation for each population member.
            constraint_violation has shape ``(np.size(population, 0), M)``,
            where M is the number of constraints.
        """
        num_members = np.size(population, 0)
        if not self._wrapped_constraints:
            # shortcut for no constraints
            return np.ones(num_members, bool), np.zeros((num_members, 1))

        # (S, N)
        parameters_pop = self._scale_parameters(population)

        if self.vectorized:
            # (S, M)
            constraint_violation = np.array(
                self._constraint_violation_fn(parameters_pop)
            )
        else:
            # (S, 1, M)
            constraint_violation = np.array([self._constraint_violation_fn(x)
                                             for x in parameters_pop])
            # if you use the list comprehension in the line above it will
            # create an array of shape (S, 1, M), because each iteration
            # generates an array of (1, M). In comparison the vectorized
            # version returns (S, M). It's therefore necessary to remove axis 1
            constraint_violation = constraint_violation[:, 0]

        feasible = ~(np.sum(constraint_violation, axis=1) > 0)

        return feasible, constraint_violation

    def __iter__(self):
        return self

    def __enter__(self):
        return self

    def __exit__(self, *args):
        return self._mapwrapper.__exit__(*args)

    def _accept_trial(self, energy_trial, feasible_trial, cv_trial,
                      energy_orig, feasible_orig, cv_orig):
        """
        Trial is accepted if:
        * it satisfies all constraints and provides a lower or equal objective
          function value, while both the compared solutions are feasible
        - or -
        * it is feasible while the original solution is infeasible,
        - or -
        * it is infeasible, but provides a lower or equal constraint violation
          for all constraint functions.

        This test corresponds to section III of Lampinen [1]_.

        Parameters
        ----------
        energy_trial : float
            Energy of the trial solution
        feasible_trial : float
            Feasibility of trial solution
        cv_trial : array-like
            Excess constraint violation for the trial solution
        energy_orig : float
            Energy of the original solution
        feasible_orig : float
            Feasibility of original solution
        cv_orig : array-like
            Excess constraint violation for the original solution

        Returns
        -------
        accepted : bool

        """
        if feasible_orig and feasible_trial:
            return energy_trial <= energy_orig
        elif feasible_trial and not feasible_orig:
            return True
        elif not feasible_trial and (cv_trial <= cv_orig).all():
            # cv_trial < cv_orig would imply that both trial and orig are not
            # feasible
            return True

        return False

    def __next__(self):
        """
        Evolve the population by a single generation

        Returns
        -------
        x : ndarray
            The best solution from the solver.
        fun : float
            Value of objective function obtained from the best solution.
        """
        # the population may have just been initialized (all entries are
        # np.inf). If it has you have to calculate the initial energies
        if np.all(np.isinf(self.population_energies)):
            self.feasible, self.constraint_violation = (
                self._calculate_population_feasibilities(self.population))

            # only need to work out population energies for those that are
            # feasible
            self.population_energies[self.feasible] = (
                self._calculate_population_energies(
                    self.population[self.feasible]))

            self._promote_lowest_energy()

        if self.dither is not None:
            self.scale = self.random_number_generator.uniform(self.dither[0],
                                                              self.dither[1])

        if self._updating == 'immediate':
            # update best solution immediately
            for candidate in range(self.num_population_members):
                if self._nfev > self.maxfun:
                    raise StopIteration

                # create a trial solution
                trial = self._mutate(candidate)

                # ensuring that it's in the range [0, 1)
                self._ensure_constraint(trial)

                # scale from [0, 1) to the actual parameter value
                parameters = self._scale_parameters(trial)

                # determine the energy of the objective function
                if self._wrapped_constraints:
                    cv = self._constraint_violation_fn(parameters)
                    feasible = False
                    energy = np.inf
                    if not np.sum(cv) > 0:
                        # solution is feasible
                        feasible = True
                        energy = self.func(parameters)
                        self._nfev += 1
                else:
                    feasible = True
                    cv = np.atleast_2d([0.])
                    energy = self.func(parameters)
                    self._nfev += 1

                # compare trial and population member
                if self._accept_trial(energy, feasible, cv,
                                      self.population_energies[candidate],
                                      self.feasible[candidate],
                                      self.constraint_violation[candidate]):
                    self.population[candidate] = trial
                    self.population_energies[candidate] = np.squeeze(energy)
                    self.feasible[candidate] = feasible
                    self.constraint_violation[candidate] = cv

                    # if the trial candidate is also better than the best
                    # solution then promote it.
                    if self._accept_trial(energy, feasible, cv,
                                          self.population_energies[0],
                                          self.feasible[0],
                                          self.constraint_violation[0]):
                        self._promote_lowest_energy()

        elif self._updating == 'deferred':
            # update best solution once per generation
            if self._nfev >= self.maxfun:
                raise StopIteration

            # 'deferred' approach, vectorised form.
            # create trial solutions
            trial_pop = np.array(
                [self._mutate(i) for i in range(self.num_population_members)])

            # enforce bounds
            self._ensure_constraint(trial_pop)

            # determine the energies of the objective function, but only for
            # feasible trials
            feasible, cv = self._calculate_population_feasibilities(trial_pop)
            trial_energies = np.full(self.num_population_members, np.inf)

            # only calculate for feasible entries
            trial_energies[feasible] = self._calculate_population_energies(
                trial_pop[feasible])

            # which solutions are 'improved'?
            loc = [self._accept_trial(*val) for val in
                   zip(trial_energies, feasible, cv, self.population_energies,
                       self.feasible, self.constraint_violation)]
            loc = np.array(loc)
            self.population = np.where(loc[:, np.newaxis],
                                       trial_pop,
                                       self.population)
            self.population_energies = np.where(loc,
                                                trial_energies,
                                                self.population_energies)
            self.feasible = np.where(loc,
                                     feasible,
                                     self.feasible)
            self.constraint_violation = np.where(loc[:, np.newaxis],
                                                 cv,
                                                 self.constraint_violation)

            # make sure the best solution is updated if updating='deferred'.
            # put the lowest energy into the best solution position.
            self._promote_lowest_energy()

        return self.x, self.population_energies[0]

    def _scale_parameters(self, trial):
        """Scale from a number between 0 and 1 to parameters."""
        # trial either has shape (N, ) or (L, N), where L is the number of
        # solutions being scaled
        scaled = self.__scale_arg1 + (trial - 0.5) * self.__scale_arg2
        if np.any(self.integrality):
            i = np.broadcast_to(self.integrality, scaled.shape)
            scaled[i] = np.round(scaled[i])
        return scaled

    def _unscale_parameters(self, parameters):
        """Scale from parameters to a number between 0 and 1."""
        return (parameters - self.__scale_arg1) / self.__scale_arg2 + 0.5

    def _ensure_constraint(self, trial):
        """Make sure the parameters lie between the limits."""
        mask = np.where((trial > 1) | (trial < 0))
        trial[mask] = self.random_number_generator.uniform(size=mask[0].shape)

    def _mutate(self, candidate):
        """Create a trial vector based on a mutation strategy."""
        trial = np.copy(self.population[candidate])

        rng = self.random_number_generator

        fill_point = rng.choice(self.parameter_count)

        if self.strategy in ['currenttobest1exp', 'currenttobest1bin']:
            bprime = self.mutation_func(candidate,
                                        self._select_samples(candidate, 5))
        else:
            bprime = self.mutation_func(self._select_samples(candidate, 5))

        if self.strategy in self._binomial:
            crossovers = rng.uniform(size=self.parameter_count)
            crossovers = crossovers < self.cross_over_probability
            # the last one is always from the bprime vector for binomial
            # If you fill in modulo with a loop you have to set the last one to
            # true. If you don't use a loop then you can have any random entry
            # be True.
            crossovers[fill_point] = True
            trial = np.where(crossovers, bprime, trial)
            return trial

        elif self.strategy in self._exponential:
            i = 0
            crossovers = rng.uniform(size=self.parameter_count)
            crossovers = crossovers < self.cross_over_probability
            crossovers[0] = True
            while (i < self.parameter_count and crossovers[i]):
                trial[fill_point] = bprime[fill_point]
                fill_point = (fill_point + 1) % self.parameter_count
                i += 1

            return trial

    def _best1(self, samples):
        """best1bin, best1exp"""
        r0, r1 = samples[:2]
        return (self.population[0] + self.scale *
                (self.population[r0] - self.population[r1]))

    def _rand1(self, samples):
        """rand1bin, rand1exp"""
        r0, r1, r2 = samples[:3]
        return (self.population[r0] + self.scale *
                (self.population[r1] - self.population[r2]))

    def _randtobest1(self, samples):
        """randtobest1bin, randtobest1exp"""
        r0, r1, r2 = samples[:3]
        bprime = np.copy(self.population[r0])
        bprime += self.scale * (self.population[0] - bprime)
        bprime += self.scale * (self.population[r1] -
                                self.population[r2])
        return bprime

    def _currenttobest1(self, candidate, samples):
        """currenttobest1bin, currenttobest1exp"""
        r0, r1 = samples[:2]
        bprime = (self.population[candidate] + self.scale *
                  (self.population[0] - self.population[candidate] +
                   self.population[r0] - self.population[r1]))
        return bprime

    def _best2(self, samples):
        """best2bin, best2exp"""
        r0, r1, r2, r3 = samples[:4]
        bprime = (self.population[0] + self.scale *
                  (self.population[r0] + self.population[r1] -
                   self.population[r2] - self.population[r3]))

        return bprime

    def _rand2(self, samples):
        """rand2bin, rand2exp"""
        r0, r1, r2, r3, r4 = samples
        bprime = (self.population[r0] + self.scale *
                  (self.population[r1] + self.population[r2] -
                   self.population[r3] - self.population[r4]))

        return bprime

    def _select_samples(self, candidate, number_samples):
        """
        obtain random integers from range(self.num_population_members),
        without replacement. You can't have the original candidate either.
        """
        idxs = list(range(self.num_population_members))
        idxs.remove(candidate)
        self.random_number_generator.shuffle(idxs)
        idxs = idxs[:number_samples]
        return idxs


class _ConstraintWrapper:
    """Object to wrap/evaluate user defined constraints.

    Very similar in practice to `PreparedConstraint`, except that no evaluation
    of jac/hess is performed (explicit or implicit).

    If created successfully, it will contain the attributes listed below.

    Parameters
    ----------
    constraint : {`NonlinearConstraint`, `LinearConstraint`, `Bounds`}
        Constraint to check and prepare.
    x0 : array_like
        Initial vector of independent variables, shape (N,)

    Attributes
    ----------
    fun : callable
        Function defining the constraint wrapped by one of the convenience
        classes.
    bounds : 2-tuple
        Contains lower and upper bounds for the constraints --- lb and ub.
        These are converted to ndarray and have a size equal to the number of
        the constraints.
    """
    def __init__(self, constraint, x0):
        self.constraint = constraint

        if isinstance(constraint, NonlinearConstraint):
            def fun(x):
                x = np.asarray(x)
                return np.atleast_1d(constraint.fun(x))
        elif isinstance(constraint, LinearConstraint):
            def fun(x):
                if issparse(constraint.A):
                    A = constraint.A
                else:
                    A = np.atleast_2d(constraint.A)
                return A.dot(x)
        elif isinstance(constraint, Bounds):
            def fun(x):
                return np.asarray(x)
        else:
            raise ValueError("`constraint` of an unknown type is passed.")

        self.fun = fun

        lb = np.asarray(constraint.lb, dtype=float)
        ub = np.asarray(constraint.ub, dtype=float)

        x0 = np.asarray(x0)

        # find out the number of constraints
        f0 = fun(x0)
        self.num_constr = m = f0.size
        self.parameter_count = x0.size

        if lb.ndim == 0:
            lb = np.resize(lb, m)
        if ub.ndim == 0:
            ub = np.resize(ub, m)

        self.bounds = (lb, ub)

    def __call__(self, x):
        return np.atleast_1d(self.fun(x))

    def violation(self, x):
        """How much the constraint is exceeded by.

        Parameters
        ----------
        x : array-like
            Vector of independent variables, (N, S), where N is number of
            parameters and S is the number of solutions to be investigated.

        Returns
        -------
        excess : array-like
            How much the constraint is exceeded by, for each of the
            constraints specified by `_ConstraintWrapper.fun`.
            Has shape (M, S) where M is the number of constraint components.
        """
        # expect ev to have shape (num_constr, S) or (num_constr,)
        ev = self.fun(np.asarray(x))

        try:
            excess_lb = np.maximum(self.bounds[0] - ev.T, 0)
            excess_ub = np.maximum(ev.T - self.bounds[1], 0)
        except ValueError as e:
            raise RuntimeError("An array returned from a Constraint has"
                               " the wrong shape. If `vectorized is False`"
                               " the Constraint should return an array of"
                               " shape (M,). If `vectorized is True` then"
                               " the Constraint must return an array of"
                               " shape (M, S), where S is the number of"
                               " solution vectors and M is the number of"
                               " constraint components in a given"
                               " Constraint object.") from e

        v = (excess_lb + excess_ub).T
        return v