`DEEMI` — improved variant¶

from DEEM.DEEMI import DEEM      # same class name, improved implementation

DEEMI shares the base interface and adds the arguments below. Its defaults enable the improvements; set method_reset="density" and adapt=False to reproduce the original behaviour. See the DEEMI overview for the ideas behind each option.

Sub-population argument name

In DEEMI the number of sub-populations is passed as npop_max / npop_min (the base DEEM class uses nswarm_max / nswarm_min). Both name the same quantity \(n_s\).

Constructor¶

DEEM(
    function, lower_bound, upper_bound, X0=None,
    nparticles_max=50, nparticles_min=50,
    npop_max=10, npop_min=2,
    maxiter=1000, maxfev=100000000,
    sampling_method='LHS',
    nworkers=1,
    tolerance=1e-6, termination='iterations',
    maxiter_below_tolerance=30, log_interval=1,
    method_subswarm_reduction='sigmoid-3',
    method_boundary='damping',
    method_subswarm_creation='equally-distributed',
    method_reset='hybrid',
    niter_reset_global=None,
    penalty=1e22,
    adapt=True,
    cache_tol=None,
    surrogate=None,
    seed=None,
    restart_budget=None,
)

Arguments shared with the base class¶

All arguments documented in the DEEM reference apply, except that nswarm_max / nswarm_min are named npop_max / npop_min here, and method_reset defaults to "hybrid".

Arguments added by DEEMI¶

Argument	Default	Description
`method_reset`	`'hybrid'`	`'hybrid'` (improved restart) or `'density'` (original).
`adapt`	`True`	SHADE-style improvement-weighted adaptation of \(CR\) and \(\phi\).
`cache_tol`	`None`	Relative tolerance of the evaluation cache; `None` disables it.
`surrogate`	`None`	Surrogate manager for pre-screening (e.g. `SurrogateManager`); `None` disables it.
`seed`	`None`	Seed for NumPy's global RNG (reproducibility).
`restart_budget`	`None`	Maximum number of global restarts (`None` = unlimited).

Methods¶

`update()`¶

Runs the optimisation loop and returns a result dictionary:

result = optimizer.update()

Key	Description
`x`	Best position found, \(\mathbf{x}_{GB}\).
`f`	Best objective value found.
`nit`	Number of iterations executed.
`nfev`	Number of real objective evaluations performed.
`n_restarts`	Number of global restarts triggered.
`cache_size`	Number of cached evaluations (0 if the cache is off).
`time`	Wall-clock run time in seconds.

The result attributes XBEST, FBEST and fev are also available, as in the base class.

Surrogate managers¶

Surrogate models are experimental

The surrogate models are currently experimental. In our experience they do not necessarily improve the optimisation process in all cases, and further investigation is required. We recommend running the optimisation without a surrogate model first.

Two interchangeable surrogate controllers are bundled in DEEM.surrogate. Both expose the same select(optimizer, candidates) / observe(evaluated) interface and a kNN feasibility bias; they differ in the underlying model.

`SurrogateManager` (RBF)¶

from DEEM.surrogate import SurrogateManager

SurrogateManager(LB, UB, eval_frac=0.5, explore_frac=0.3,
                 min_train=40, refit_every=10)

Argument	Default	Description
`LB`, `UB`	—	Search-space bounds (used to scale the surrogate inputs).
`eval_frac`	`0.5`	Fraction of candidates evaluated on the real objective each iteration.
`explore_frac`	`0.3`	Additional fraction reserved for exploration / novelty.
`min_train`	`40`	Minimum number of observations before the surrogate is used.
`refit_every`	`10`	Refit the surrogate every n iterations.

A regularised radial-basis-function interpolant; ranks candidates by predicted value plus a nearest-neighbour novelty score.

`GPSurrogateManager` (Gaussian process)¶

from DEEM.surrogate import GPSurrogateManager

GPSurrogateManager(LB, UB, eval_frac=0.5, explore_frac=0.3,
                   kappa=1.5, min_train=40, refit_every=10)

Argument	Default	Description
`LB`, `UB`	—	Search-space bounds.
`eval_frac`	`0.5`	Fraction of candidates evaluated on the real objective each iteration.
`explore_frac`	`0.3`	Fraction of the budget reserved for the most uncertain candidates.
`kappa`	`1.5`	Exploration weight of the Lower-Confidence-Bound acquisition \(\mu - \kappa\sigma\).
`min_train`	`40`	Minimum number of observations before pre-screening starts.
`refit_every`	`10`	Refit the GP every n new observations.

A Gaussian process with a squared-exponential kernel; selects by the Lower-Confidence-Bound rule, so it evaluates candidates that are either promising (low posterior mean) or uncertain (high posterior std).

Pass either manager to DEEM(..., surrogate=...). Both depend only on numpy and scipy.

DEEMI — improved variant¶