NAG CL Interface
e05sbc (nlp_pso)
Note: this function uses optional parameters to define choices in the problem specification and in the details of the algorithm. If you wish to use default
settings for all of the optional parameters, you need only read Sections 1 to 10 of this document. If, however, you wish to reset some or all of the settings please refer to Section 11 for a detailed description of the algorithm and to Section 12 for a detailed description of the specification of the optional parameters.
1
Purpose
e05sbc is designed to search for the global minimum or maximum of an arbitrary function, subject to general nonlinear constraints, using Particle Swarm Optimization (PSO). Derivatives are not required, although these may be used by an accompanying local minimization function if desired.
e05sbc is essentially identical to
e05sac, with an expert interface and various additional arguments added; otherwise most arguments are identical. In particular,
e05sac does not handle general constraints.
2
Specification
void 
e05sbc (Integer ndim,
Integer ncon,
Integer npar,
double xb[],
double *fb,
double cb[],
const double bl[],
const double bu[],
double xbest[],
double fbest[],
double cbest[],
void 
(*confun)(Integer *mode,
Integer ncon,
Integer ndim,
Integer tdcj,
const Integer needc[],
const double x[],
double c[],
double cjac[],
Integer nstate,
Nag_Comm *comm),


void 
(*monmod)(Integer ndim,
Integer ncon,
Integer npar,
double x[],
const double xb[],
double fb,
const double cb[],
const double xbest[],
const double fbest[],
const double cbest[],
const Integer itt[],
Nag_Comm *comm, Integer *inform),


Integer iopts[],
double opts[],
Nag_Comm *comm, Integer itt[],
Integer *inform,
NagError *fail) 

The function may be called by the names: e05sbc or nag_glopt_nlp_pso.
Before calling
e05sbc,
e05zkc must be called with
optstr set to ‘
Initialize = e05sbc’. Optional parameters may also be specified by calling
e05zkc before the call to
e05sbc.
3
Description
e05sbc uses a stochastic method based on Particle Swarm Optimization (PSO) to search for the global optimum of a nonlinear function
$F$, subject to a set of bound constraints on the variables, and optionally a set of general nonlinear constraints. In the PSO algorithm (see
Section 11), a set of particles is generated in the search space, and advances each iteration to (hopefully) better positions using a heuristic velocity based upon
inertia,
cognitive memory and
global memory. The inertia is provided by a decreasingly weighted contribution from a particles current velocity, the cognitive memory refers to the best candidate found by an individual particle and the global memory refers to the best candidate found by all the particles. This allows for a global search of the domain in question.
Further, this may be coupled with a selection of local minimization functions, which may be called during the iterations of the heuristic algorithm, the interior phase, to hasten the discovery of locally optimal points, and after the heuristic phase has completed to attempt to refine the final solution, the exterior phase. Different options may be set for the local optimizer in each phase.
Without loss of generality, the problem is assumed to be stated in the following form:
where the objective
$F\left(\mathbf{x}\right)$ is a scalar function,
$\mathbf{c}\left(\mathbf{x}\right)$ is a vector of scalar constraint functions,
$\mathbf{x}$ is a vector in
${R}^{\mathit{ndim}}$ and the vectors
$\mathbf{\ell}\le \mathbf{u}$ are lower and upper bounds respectively for the
$\mathit{ndim}$ variables and
$\mathit{ncon}$ constraints. Both the objective function and the
$\mathit{ncon}$ constraints may be nonlinear. Continuity of
$F$, and the functions
$\mathbf{c}\left(\mathbf{x}\right)$, is not essential. For functions which are smooth and primarily unimodal, faster solutions will almost certainly be achieved by using
Chapter E04 functions directly.
For functions which are smooth and multimodal, gradient dependent local minimization functions may be coupled with e05sbc.
For multimodal functions for which derivatives cannot be provided, particularly functions with a significant level of noise in their evaluation,
e05sbc should be used either alone, or coupled with
e04cbc.
For heavily constrained problems,
e05sbc should either be used alone, or coupled with
e04ucc provided the function and the constraints are sufficiently smooth.
The
$\mathit{ndim}$ lower and upper box bounds on the variable
$\mathbf{x}$ are included to initialize the particle swarm into a finite hypervolume, although their subsequent influence on the algorithm is user determinable (see the option
${\mathbf{Boundary}}$ in
Section 12). It is strongly recommended that sensible bounds are provided for all variables and constraints.
e05sbc may also be used to maximize the objective function, or to search for a feasible point satisfying the simple bounds and general constraints (see the option ${\mathbf{Optimize}}$).
Due to the nature of global optimization, unless a predefined target is provided, there is no definitive way of knowing when to end a computation. As such several stopping heuristics have been implemented into the algorithm. If any of these is achieved,
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED, and the parameter
inform will indicate which criteria was reached. See
inform for more information.
In addition, you may provide your own stopping criteria through
monmod,
objfun and
confun.
e05sac provides a simpler interface, without the inclusion of general nonlinear constraints.
4
References
Gill P E, Murray W and Wright M H (1981) Practical Optimization Academic Press
Kennedy J and Eberhart R C (1995) Particle Swarm Optimization Proceedings of the 1995 IEEE International Conference on Neural Networks 1942–1948
Koh B, George A D, Haftka R T and Fregly B J (2006) Parallel Asynchronous Particle Swarm Optimization International Journal for Numerical Methods in Engineering 67(4) 578–595
Vaz A I and Vicente L N (2007) A Particle Swarm Pattern Search Method for Bound Constrained Global Optimization Journal of Global Optimization 39(2) 197–219 Kluwer Academic Publishers
5
Arguments
Note: for descriptions of the symbolic variables, see
Section 11.

1:
$\mathbf{ndim}$ – Integer
Input

On entry: $\mathit{ndim}$, the number of dimensions.
Constraint:
${\mathbf{ndim}}\ge 1$.

2:
$\mathbf{ncon}$ – Integer
Input

On entry: $\mathit{ncon}$, the number of constraints, not including box constraints.
Constraint:
${\mathbf{ncon}}\ge 0$.

3:
$\mathbf{npar}$ – Integer
Input

On entry:
$\mathit{npar}$, the number of particles to be used in the swarm. Assuming all particles remain within constraints, each complete iteration will perform at least
npar function evaluations. Otherwise, significantly fewer objective function evaluations may be performed.
Suggested value:
${\mathbf{npar}}=10\times {\mathbf{ndim}}$.
Constraint:
${\mathbf{npar}}\ge 5$.

4:
$\mathbf{xb}\left[{\mathbf{ndim}}\right]$ – double
Output

On exit: the location of the best solution found, $\stackrel{~}{\mathbf{x}}$, in ${R}^{\mathit{ndim}}$.

5:
$\mathbf{fb}$ – double *
Output

On exit: the objective value of the best solution, $\stackrel{~}{f}=F\left(\stackrel{~}{\mathbf{x}}\right)$.

6:
$\mathbf{cb}\left[{\mathbf{ncon}}\right]$ – double
Output

On exit: the constraint violations of the best solution found,
$\stackrel{~}{\mathbf{e}}=\mathbf{e}\left(\stackrel{~}{\mathbf{x}}\right)$. These may have been deemed to be acceptable given the tolerance and scaling of the constraints. See
Sections 11 and
12.

7:
$\mathbf{bl}\left[{\mathbf{ndim}}+{\mathbf{ncon}}\right]$ – const double
Input

8:
$\mathbf{bu}\left[{\mathbf{ndim}}+{\mathbf{ncon}}\right]$ – const double
Input

On entry:
${\mathbf{bl}}$ is
$\mathbf{\ell}$, the array of lower bounds,
bu is
$\mathbf{u}$, the array of upper bounds. The first
ndim entries in
bl and
bu must contain the lower and upper simple (box) bounds of the variables respectively. These must be provided to initialize the sample population into a finite hypervolume, although their subsequent influence on the algorithm is user determinable (see the option
${\mathbf{Boundary}}$ in
Section 12).
The next
ncon entries must contain the lower and upper bounds for any general constraints respectively.
If ${\mathbf{bl}}\left[i1\right]={\mathbf{bu}}\left[i1\right]$ for any $i\in \left\{1,\dots ,{\mathbf{ndim}}\right\}$, variable $i$ will remain locked to ${\mathbf{bl}}\left[i1\right]$ regardless of the ${\mathbf{Boundary}}$ option selected.
It is strongly advised that you place sensible lower and upper bounds on all variables and constraints, even if your model allows for unbounded variables or constraints.
Constraints:
 ${\mathbf{bl}}\left[\mathit{i}1\right]\le {\mathbf{bu}}\left[\mathit{i}1\right]$, for $\mathit{i}=1,2,\dots ,{\mathbf{ndim}}+{\mathbf{ncon}}$;
 ${\mathbf{bl}}\left[i1\right]\ne {\mathbf{bu}}\left[i1\right]$ for at least one $i\in \left\{1,\dots ,{\mathbf{ndim}}\right\}$.

9:
$\mathbf{xbest}\left[{\mathbf{ndim}}\times {\mathbf{npar}}\right]$ – double
Input/Output

Note: the $i$th component of the best position of the $j$th particle, ${\hat{x}}_{j}\left(i\right)$, is stored in ${\mathbf{xbest}}\left[\left(j1\right)\times {\mathbf{ndim}}+i1\right]$.
On entry: if using ${\mathbf{Start}}=\mathrm{WARM}$, the initial particle positions, ${\hat{\mathbf{x}}}_{j}^{0}$.
On exit: the best positions found,
${\hat{\mathbf{x}}}_{j}$, by the
npar particles in the swarm.

10:
$\mathbf{fbest}\left[{\mathbf{npar}}\right]$ – double
Input/Output

On entry: if using
${\mathbf{Start}}=\mathrm{WARM}$, objective function values,
${\hat{f}}_{j}^{0}=F\left({\hat{\mathbf{x}}}_{j}^{0}\right)$, corresponding to the
npar particle locations stored in
xbest.
On exit: objective function values,
${\hat{f}}_{j}=F\left({\hat{\mathbf{x}}}_{j}\right)$, corresponding to the locations returned in
xbest.

11:
$\mathbf{cbest}\left[{\mathbf{ncon}}\times {\mathbf{npar}}\right]$ – double
Input/Output

Note: the $k$th constraint violation of the $j$th particle is stored in ${\mathbf{cbest}}\left[\left(j1\right)\times {\mathbf{ncon}}+k1\right]$.
On entry: if using
${\mathbf{Start}}=\mathrm{WARM}$, the initial constraint violations,
${\hat{\mathbf{e}}}_{j}^{0}=\mathbf{e}\left({\hat{\mathbf{x}}}_{j}^{0}\right)$, corresponding to the
npar particle locations.
On exit: the final constraint violations,
${\hat{\mathbf{e}}}_{j}$, corresponding to the locations returned in
xbest.

12:
$\mathbf{objfun}$ – function, supplied by the user
External Function

objfun must, depending on the value of
mode, calculate the objective function
and/or calculate the gradient of the objective function for a
$\mathit{ndim}$variable vector
$\mathbf{x}$. Gradients are only required if a local minimizer has been chosen which requires gradients. See the option
${\mathbf{Local\; Minimizer}}$ for more information.
The specification of
objfun is:

1:
$\mathbf{mode}$ – Integer *
Input/Output

On entry: indicates which functionality is required.
 ${\mathbf{mode}}=0$
 $F\left(\mathbf{x}\right)$ should be returned in objf. The value of objf on entry may be used as an upper bound for the calculation. Any expected value of $F\left(\mathbf{x}\right)$ that is greater than objf may be approximated by this upper bound; that is objf can remain unaltered.
 ${\mathbf{mode}}=1$
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04ucc}$ only
First derivatives can be evaluated and returned in vecout. Any unaltered elements of vecout will be approximated using finite differences.
 ${\mathbf{mode}}=2$
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04ucc}$ only
$F\left(\mathbf{x}\right)$ must be calculated and returned in objf, and available first derivatives can be evaluated and returned in vecout. Any unaltered elements of vecout will be approximated using finite differences.
 ${\mathbf{mode}}=5$
 $F\left(\mathbf{x}\right)$ must be calculated and returned in objf. The value of objf on entry may not be used as an upper bound.
 ${\mathbf{mode}}=6$
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04dgc}$ only
All first derivatives must be evaluated and returned in vecout.
 ${\mathbf{mode}}=7$
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04dgc}$ only
$F\left(\mathbf{x}\right)$ must be calculated and returned in objf, and all first derivatives must be evaluated and returned in vecout.
On exit: if the value of
mode is set to be negative,
e05sbc will exit as soon as possible with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_USER_STOP and
${\mathbf{inform}}={\mathbf{mode}}$.

2:
$\mathbf{ndim}$ – Integer
Input

On entry: the number of dimensions.

3:
$\mathbf{x}\left[{\mathbf{ndim}}\right]$ – const double
Input

On entry: $\mathbf{x}$, the point at which the objective function and/or its gradient are to be evaluated.

4:
$\mathbf{objf}$ – double *
Input/Output

On entry: the value of
objf passed to
objfun varies with the argument
mode.
 ${\mathbf{mode}}=0$
 objf is an upper bound for the value of $F\left(\mathbf{x}\right)$, often equal to the best constraint penalised value of $F\left(\mathbf{x}\right)$ found so far by a given particle if the objective function is strictly positive (see Section 11). Only objective function values less than the value of objf on entry will be used further. As such this upper bound may be used to stop further evaluation when this will only increase the objective function value above the upper bound.
 ${\mathbf{mode}}=1$, $2$, $5$, $6$ or $7$
 objf is meaningless on entry.
On exit: the value of
objf returned varies with the argument
mode.
 ${\mathbf{mode}}=0$
 objf must be the value of $F\left(\mathbf{x}\right)$. Only values of $F\left(\mathbf{x}\right)$ strictly less than objf on entry need be accurate.
 ${\mathbf{mode}}=1$ or $6$
 Need not be set.
 ${\mathbf{mode}}=2$, $5$ or $7$
 $F\left(\mathbf{x}\right)$ must be calculated and returned in objf. The entry value of objf may not be used as an upper bound.

5:
$\mathbf{vecout}\left[{\mathbf{ndim}}\right]$ – double
Input/Output

On entry: if
${\mathbf{Local\; Minimizer}}=\mathrm{e04ucc}$,
the values of
vecout are used internally to indicate whether a finite difference approximation is required. See
e04ucc.
On exit: the required values of
vecout returned to the calling function depend on the value of
mode.
 ${\mathbf{mode}}=0$ or $5$
 The value of vecout need not be set.
 ${\mathbf{mode}}=1$ or $2$
 vecout can contain components of the gradient of the objective function $\frac{\partial F}{\partial {x}_{i}}$ for some $i=1,2,\dots {\mathbf{ndim}}$, or acceptable approximations. Any unaltered elements of vecout will be approximated using finite differences.
 ${\mathbf{mode}}=6$ or $7$
 vecout must contain the gradient of the objective function $\frac{\partial F}{\partial {x}_{i}}$ for all $i=1,2,\dots {\mathbf{ndim}}$. Approximation of the gradient is strongly discouraged, and no finite difference approximations will be performed internally (see e04dgc).

6:
$\mathbf{nstate}$ – Integer
Input

On entry:
nstate indicates various stages of initialization throughout the function. This allows for permanent global arguments to be initialized the least number of times. For example, you may initialize a random number generator seed.
 ${\mathbf{nstate}}=2$
 objfun is called for the very first time. You may save computational time if certain data must be read or calculated only once.
 ${\mathbf{nstate}}=1$
 objfun is called for the first time by a NAG local minimization function. You may save computational time if certain data required for the local minimizer need only be calculated at the initial point of the local minimization.
 ${\mathbf{nstate}}=0$
 Used in all other cases.

7:
$\mathbf{comm}$ – Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to
objfun.
 user – double *
 iuser – Integer *
 p – Pointer
The type Pointer will be
void *. Before calling
e05sbc you may allocate memory and initialize these pointers with various quantities for use by
objfun when called from
e05sbc (see
Section 3.1.1 in the Introduction to the NAG Library CL Interface).
Note: objfun should not return floatingpoint NaN (Not a Number) or infinity values, since these are not handled by
e05sbc. If your code inadvertently
does return any NaNs or infinities,
e05sbc is likely to produce unexpected results.

13:
$\mathbf{confun}$ – function, supplied by the user
External Function

confun must calculate any constraints other than the box constraints. If no constraints are required,
confun may be
NULLFN.
For information on how a NAG local minimizer will use
confun see the documentation for
e04ucc.
The specification of
confun is:

1:
$\mathbf{mode}$ – Integer *
Input/Output

On entry: indicates which values must be assigned during each call of
confun. Only the following values need be assigned, for each value of
$k\in \left\{1,\dots ,{\mathbf{ncon}}\right\}$ such that
${\mathbf{needc}}\left[k1\right]>0$:
 ${\mathbf{mode}}=0$
 the constraint values ${c}_{k}\left(\mathbf{x}\right)$.
 ${\mathbf{mode}}=1$
 rows of the constraint Jacobian,
$\frac{\partial {c}_{k}}{\partial {x}_{\mathit{i}}}\left(\mathbf{x}\right)$, for $\mathit{i}=1,2,\dots ,{\mathbf{ndim}}$.
 ${\mathbf{mode}}=2$
 the constraint values ${c}_{k}\left(\mathbf{x}\right)$ and the corresponding rows of the constraint Jacobian,
$\frac{\partial {c}_{k}}{\partial {x}_{\mathit{i}}}\left(\mathbf{x}\right)$, for $\mathit{i}=1,2,\dots ,{\mathbf{ndim}}$.
On exit: may be set to a negative value if you wish to terminate the solution to the current problem. In this case
e05sbc will terminate with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_USER_STOP and
${\mathbf{inform}}={\mathbf{mode}}$ as soon as possible.

2:
$\mathbf{ncon}$ – Integer
Input

On entry: the number of constraints, not including box bounds.

3:
$\mathbf{ndim}$ – Integer
Input

On entry: the number of variables.

4:
$\mathbf{tdcj}$ – Integer
Input

On entry: the stride separating matrix column elements in the array
cjac.

5:
$\mathbf{needc}\left[{\mathbf{ncon}}\right]$ – const Integer
Input

On entry: the indices of the elements of
c and/or
cjac that must be evaluated by
confun. If
${\mathbf{needc}}\left[k1\right]>0$, the
$k1$th element of
c, corresponding to the values of the
$k$th constraint, and/or the available elements of the
$k1$th row of
cjac, corresponding to the derivatives of the
$k$th constraint, must be evaluated at
$\mathbf{x}$ (see argument
mode).

6:
$\mathbf{x}\left[{\mathbf{ndim}}\right]$ – const double
Input

On entry: $\mathbf{x}$, the vector of variables at which the constraint functions and/or the available elements of the constraint Jacobian are to be evaluated.

7:
$\mathbf{c}\left[{\mathbf{ncon}}\right]$ – double
Output

On exit: if
${\mathbf{needc}}\left[k1\right]>0$ and
${\mathbf{mode}}=0$ or
$2$,
${\mathbf{c}}\left[k1\right]$ must contain the value of
${c}_{k}\left(\mathbf{x}\right)$. The remaining elements of
c, corresponding to the nonpositive elements of
needc, need not be set.

8:
$\mathbf{cjac}\left[{\mathbf{ncon}}\times {\mathbf{tdcj}}\right]$ – double
Input/Output

Note: the dimension,
dim, of the array
cjac
is
${\mathbf{tdcj}}\times {\mathbf{ndim}}$.
the derivative of the $k$th constraint with respect to the $i$th component, $\frac{\partial {c}_{k}}{\partial {x}_{\mathrm{i}}}$, is stored in ${\mathbf{cjac}}[\left(k1\right)\times {\mathbf{tdcj}}+i1]$, which we denote as ${\mathbf{CJAC}}\left(k,i\right)$.
On entry: the elements of
cjac are set to special values which enable
e05sbc to detect whether they are changed by
confun.
On exit: if
${\mathbf{needc}}\left[k1\right]>0$ and
${\mathbf{mode}}=1$ or
$2$, the elements of
cjac corresponding to the
$k$th row of the constraint Jacobian should contain the available elements of the vector
$\nabla {c}_{k}$ given by
where
$\frac{\partial {c}_{k}}{\partial {x}_{i}}$ is the partial derivative of the
$k$th constraint with respect to the
$i$th variable, evaluated at the point
$\mathbf{x}$; elements of
cjac that remain unaltered will be approximated internally using finite differences. The remaining rows of
cjac, corresponding to nonpositive elements of
needc, need not be set.
It must be emphasized that unassigned elements of
cjac are not treated as constant; they are estimated by finite differences, at nontrivial expense. An interval for each element of
$\mathbf{x}$ is computed automatically at the start of the optimization. The automatic procedure can usually identify constant elements of
cjac, which are then computed once only by finite differences.

9:
$\mathbf{nstate}$ – Integer
Input

On entry:
nstate indicates various stages of initialization throughout the function. This allows for permanent global arguments to be initialized a minimum number of times. For example, you may initialize a random number generator seed. Note that unless the option
${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$ has been set,
objfun will be called before
confun.
 ${\mathbf{nstate}}=2$
 confun is called for the very first time. This argument setting allows you to save computational time if certain data must be read or calculated only once.
 ${\mathbf{nstate}}=1$
 confun is called for the first time during a NAG local minimization function. This argument setting allows you to save computational time if certain data required for the local minimizer need only be calculated at the initial point of the local minimization.
 ${\mathbf{nstate}}=0$
 Used in all other cases.

10:
$\mathbf{comm}$ – Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to
confun.
 user – double *
 iuser – Integer *
 p – Pointer
The type Pointer will be
void *. Before calling
e05sbc you may allocate memory and initialize these pointers with various quantities for use by
confun when called from
e05sbc (see
Section 3.1.1 in the Introduction to the NAG Library CL Interface).
Note: confun should not return floatingpoint NaN (Not a Number) or infinity values, since these are not handled by
e05sbc. If your code inadvertently
does return any NaNs or infinities,
e05sbc is likely to produce unexpected results.
confun should be tested separately before being used in conjunction with
e05sbc.

14:
$\mathbf{monmod}$ – function, supplied by the user
External Function

A userspecified monitoring and modification function.
monmod is called once every complete iteration after a finalization check. It may be used to modify the particle locations that will be evaluated at the next iteration. This permits the incorporation of algorithmic modifications such as including additional advection heuristics and genetic mutations.
monmod is only called during the main loop of the algorithm, and as such will be unaware of any further improvement from the final local minimization. If no monitoring and/or modification is required,
monmod may be NULLFN.
The specification of
monmod is:
void 
monmod (Integer ndim,
Integer ncon,
Integer npar,
double x[],
const double xb[],
double fb,
const double cb[],
const double xbest[],
const double fbest[],
const double cbest[],
const Integer itt[],
Nag_Comm *comm, Integer *inform)



1:
$\mathbf{ndim}$ – Integer
Input

On entry: the number of dimensions.

2:
$\mathbf{ncon}$ – Integer
Input

On entry: the number of constraints.

3:
$\mathbf{npar}$ – Integer
Input

On entry: the number of particles.

4:
$\mathbf{x}\left[{\mathbf{ndim}}\times {\mathbf{npar}}\right]$ – double
Input/Output

Note: the $i$th component of the $j$th particle, ${x}_{j}\left(i\right)$, is stored in ${\mathbf{x}}\left[\left(j1\right)\times {\mathbf{ndim}}+i1\right]$.
On entry: the
npar particle locations,
${\mathbf{x}}_{j}$, which will currently be used during the next iteration unless altered in
monmod.
On exit: the particle locations to be used during the next iteration.

5:
$\mathbf{xb}\left[{\mathbf{ndim}}\right]$ – const double
Input

On entry: the location, $\stackrel{~}{\mathbf{x}}$, of the best solution yet found.

6:
$\mathbf{fb}$ – double
Input

On entry: the objective value, $\stackrel{~}{f}=F\left(\stackrel{~}{\mathbf{x}}\right)$, of the best solution yet found.

7:
$\mathbf{cb}\left[{\mathbf{ncon}}\right]$ – const double
Input

On entry: the constraint violations, $\stackrel{~}{\mathbf{e}}=\mathbf{e}\left(\stackrel{~}{\mathbf{x}}\right)$, of the best solution yet found.

8:
$\mathbf{xbest}\left[{\mathbf{ndim}}\times {\mathbf{npar}}\right]$ – const double
Input

Note: the $i$th component of the position of the $j$th particle's cognitive memory, ${\hat{x}}_{j}\left(i\right)$, is stored in ${\mathbf{xbest}}\left[\left(j1\right)\times {\mathbf{ndim}}+i1\right]$.
On entry: the locations currently in the cognitive memory,
${\hat{\mathbf{x}}}_{\mathit{j}}$, for
$\mathit{j}=1,2,\dots ,{\mathbf{npar}}$ (see
Section 11).

9:
$\mathbf{fbest}\left[{\mathbf{npar}}\right]$ – const double
Input

On entry: the objective values currently in the cognitive memory,
$F\left({\hat{\mathbf{x}}}_{\mathit{j}}\right)$, for $\mathit{j}=1,2,\dots ,{\mathbf{npar}}$.

10:
$\mathbf{cbest}\left[{\mathbf{ncon}}\times {\mathbf{npar}}\right]$ – const double
Input

Note: the $k$th constraint violation of the $j$th particle's cognitive memory is stored in ${\mathbf{cbest}}\left[\left(j1\right)\times {\mathbf{ncon}}+k1\right]$.
On entry: the constraint violations currently in the cognitive memory,
$\hat{\mathbf{e}}=\mathbf{e}\left({\hat{\mathbf{x}}}_{\mathit{j}}\right)$, for $\mathit{j}=1,2,\dots ,{\mathbf{npar}}$, evaluated at ${\hat{\mathbf{x}}}_{j}$.

11:
$\mathbf{itt}\left[7\right]$ – const Integer
Input

On entry: iteration and function evaluation counters (see description of
itt below).

12:
$\mathbf{comm}$ – Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to
monmod.
 user – double *
 iuser – Integer *
 p – Pointer
The type Pointer will be
void *. Before calling
e05sbc you may allocate memory and initialize these pointers with various quantities for use by
monmod when called from
e05sbc (see
Section 3.1.1 in the Introduction to the NAG Library CL Interface).

13:
$\mathbf{inform}$ – Integer *
Input/Output

On entry: ${\mathbf{inform}}=0$
On exit: setting
${\mathbf{inform}}<0$ will cause near immediate exit from
e05sbc. This value will be returned as
inform with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_USER_STOP. You need not set
inform unless you wish to force an exit.

15:
$\mathbf{iopts}\left[\mathit{dim}\right]$ – Integer
Communication Array

Note: the dimension,
$\mathit{dim}$, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument
iopts in the previous call to
e05zkc.
On entry: optional parameter array as generated and possibly modified by calls to
e05zkc. The contents of
iopts MUST NOT be modified directly between calls to
e05sbc,
e05zkc or
e05zlc.

16:
$\mathbf{opts}\left[\mathit{dim}\right]$ – double
Communication Array

Note: the dimension,
$\mathit{dim}$, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument
opts in the previous call to
e05zkc.
On entry: optional parameter array as generated and possibly modified by calls to
e05zkc. The contents of
opts MUST NOT be modified directly between calls to
e05sbc,
e05zkc or
e05zlc.

17:
$\mathbf{comm}$ – Nag_Comm *

The NAG communication argument (see
Section 3.1.1 in the Introduction to the NAG Library CL Interface).

18:
$\mathbf{itt}\left[7\right]$ – Integer
Output

On exit: integer iteration counters for
e05sbc.
 ${\mathbf{itt}}\left[0\right]$
 Number of complete iterations.
 ${\mathbf{itt}}\left[1\right]$
 Number of complete iterations without improvement to the current optimum.
 ${\mathbf{itt}}\left[2\right]$
 Number of particles converged to the current optimum.
 ${\mathbf{itt}}\left[3\right]$
 Number of improvements to the optimum.
 ${\mathbf{itt}}\left[4\right]$
 Number of function evaluations performed.
 ${\mathbf{itt}}\left[5\right]$
 Number of particles reset.
 ${\mathbf{itt}}\left[6\right]$
 Number of violated constraints at completion. Note this is always calculated using the ${L}^{1}$ norm and a nonzero result does not necessarily mean that the algorithm did not find a suitably constrained point with respect to the single norm used.

19:
$\mathbf{inform}$ – Integer *
Output

On exit: indicates which finalization criterion was reached. The possible values of
inform are:
inform  Meaning 
$<0$  Exit from a usersupplied function. 
0  e05sbc has detected an error and terminated. 
1  The provided objective target has been achieved. (${\mathbf{Target\; Objective\; Value}}$). 
2  The standard deviation of the location of all the particles is below the set threshold (${\mathbf{Swarm\; Standard\; Deviation}}$). If the solution returned is not satisfactory, you may try setting a smaller value of ${\mathbf{Swarm\; Standard\; Deviation}}$, or try adjusting the options governing the repulsive phase (${\mathbf{Repulsion\; Initialize}}$, ${\mathbf{Repulsion\; Finalize}}$). 
3  The total number of particles converged (${\mathbf{Maximum\; Particles\; Converged}}$) to the current global optimum has reached the set limit. This is the number of particles which have moved to a distance less than ${\mathbf{Distance\; Tolerance}}$ from the optimum with regard to the ${L}^{2}$ norm. If the solution is not satisfactory, you may consider lowering the ${\mathbf{Distance\; Tolerance}}$. However, this may hinder the global search capability of the algorithm. 
4  The maximum number of iterations without improvement (${\mathbf{Maximum\; Iterations\; Static}}$) has been reached, and the required number of particles (${\mathbf{Maximum\; Iterations\; Static\; Particles}}$) have converged to the current optimum. Increasing either of these options will allow the algorithm to continue searching for longer. Alternatively if the solution is not satisfactory, restarting the application several times with ${\mathbf{Repeatability}}=\mathrm{OFF}$ may lead to an improved solution. 
5  The maximum number of iterations (${\mathbf{Maximum\; Iterations\; Completed}}$) has been reached. If the number of iterations since improvement is small, then a better solution may be found by increasing this limit, or by using the option ${\mathbf{Local\; Minimizer}}$ with corresponding exterior options. Otherwise if the solution is not satisfactory, you may try rerunning the application several times with ${\mathbf{Repeatability}}=\mathrm{OFF}$ and a lower iteration limit, or adjusting the options governing the repulsive phase (${\mathbf{Repulsion\; Initialize}}$, ${\mathbf{Repulsion\; Finalize}}$). 
6  The maximum allowed number of function evaluations (${\mathbf{Maximum\; Function\; Evaluations}}$) has been reached. As with ${\mathbf{inform}}=5$, increasing this limit if the number of iterations without improvement is small, or decreasing this limit and running the algorithm multiple times with ${\mathbf{Repeatability}}=\mathrm{OFF}$, may provide a superior result. 
7  A feasible point has been found. The objective has not been minimized, although it has been evaluated at the final solutions given in xb and xbest (${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$). 
If you wish to continue from the final position gained from a previous simulation with adjusted options, you may set the option
${\mathbf{Start}}=\mathrm{WARM}$, and pass back in the returned arrays
xbest,
fbest, and
cbest. You should either record the returned values of
xb,
fb and
cb for comparison, as these will not be reused by the algorithm, or include them in
xbest,
fbest and
cbest respectively by overwriting the entries corresponding to one particle with the relevant information.

20:
$\mathbf{fail}$ – NagError *
Input/Output

The NAG error argument (see
Section 7 in the Introduction to the NAG Library CL Interface).
e05sbc returns ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR if and only if a finalization criterion has been reached which can guarantee success. This may only happen if:
These finalization criteria are not active using default option settings, and must be explicitly set using
e05zkc if required.
e05sbc will return
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED if no error has been detected, and a finalization criterion has been achieved which cannot guarantee success. This does not indicate that the function has failed, merely that the returned solution cannot be guaranteed to be the true global optimum.
The value of
inform should be examined to determine which finalization criterion was reached.
6
Error Indicators and Warnings
 NE_ALLOC_FAIL

Dynamic memory allocation failed.
See
Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
 NE_BAD_PARAM

On entry, argument $\u2329\mathit{\text{value}}\u232a$ had an illegal value.
 NE_BOUND

On entry, ${\mathbf{bl}}\left[i\right]={\mathbf{bu}}\left[i\right]$ for all box bounds $i$.
Constraint: ${\mathbf{bu}}\left[i\right]>{\mathbf{bl}}\left[i\right]$ for at least one box bound $i$.
On entry, ${\mathbf{bl}}\left[\u2329\mathit{\text{value}}\u232a\right]=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{bu}}\left[\u2329\mathit{\text{value}}\u232a\right]=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{bu}}\left[i\right]\ge {\mathbf{bl}}\left[i\right]$ for all $i$.
 NE_DERIV_ERRORS

Derivative checks indicate possible errors in the supplied derivatives.
Gradient checks may be disabled by setting ${\mathbf{Verify\; Gradients}}=\mathrm{OFF}$.
 NE_ILLEGAL_CALLBACK

e05sbc has been called with ${\mathbf{ncon}}>0$ and ${\mathbf{confun}}\phantom{\rule{0.25em}{0ex}}\text{is}\phantom{\rule{0.25em}{0ex}}\mathbf{NULL}$. Only use NULL with ${\mathbf{ncon}}=0$.
 NE_INT

On entry, ${\mathbf{ncon}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{ncon}}\ge 0$.
On entry, ${\mathbf{ndim}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{ndim}}\ge 1$.
On entry, ${\mathbf{npar}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{npar}}\ge 5\times \mathbf{num\_threads}$, where num_threads is the value returned by the OpenMP environment variable OMP_NUM_THREADS, or num_threads is $1$ for a serial version of this function.
 NE_INTERNAL_ERROR

An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact
NAG for assistance.
See
Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
 NE_INVALID_OPTION

Either the option arrays have not been initialized for e05sbc, or they have become corrupted.
The option ${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$ is active, however ${\mathbf{ncon}}=0$.
 NE_NO_LICENCE

Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library CL Interface for further information.
 NE_USER_STOP

User requested exit
$\u2329\mathit{\text{value}}\u232a$ during call to
confun.
User requested exit
$\u2329\mathit{\text{value}}\u232a$ during call to
monmod.
User requested exit
$\u2329\mathit{\text{value}}\u232a$ during call to
objfun.
 NW_FAST_SOLUTION

If the option
${\mathbf{Target\; Warning}}$ has been activated, this indicates that the
${\mathbf{Target\; Objective\; Value}}$ has been achieved to specified tolerances at a sufficiently constrained point, either during the initialization phase, or during the first two iterations of the algorithm. While this is not necessarily an error, it may occur if:

(i)The target was achieved at the first point sampled by the function. This will be the mean of the lower and upper bounds.

(ii)The target may have been achieved at a randomly generated sample point. This will always be a possibility provided that the domain under investigation contains a point with a target objective value.

(iii)If the ${\mathbf{Local\; Minimizer}}$ has been set, then a sample point may have been inside the basin of attraction of a satisfactory point. If this occurs repeatedly when the function is called, it may imply that the objective is largely unimodal, and that it may be more efficient to use the function selected as the ${\mathbf{Local\; Minimizer}}$ directly.
Assuming that
objfun is correct, you may wish to set a better
${\mathbf{Target\; Objective\; Value}}$, or a stricter
${\mathbf{Target\; Objective\; Tolerance}}$.
 NW_NOT_FEASIBLE

Unable to locate strictly feasible point. $\u2329\mathit{\text{value}}\u232a$ constraints remain violated. This exit may be suppressed using the option ${\mathbf{Constraint\; Warning}}$.
 NW_SOLUTION_NOT_GUARANTEED

A finalization criterion was reached that cannot guarantee success.
On exit, ${\mathbf{inform}}=\u2329\mathit{\text{value}}\u232a$.
7
Accuracy
If
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR (or
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_FAST_SOLUTION) or
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED on exit, a criterion will have been reached depending on user selected options. As with all global optimization software, the solution achieved may not be the true global optimum. Various options allow for either greater search diversity or faster convergence to a (local) optimum (See
Sections 11 and
12).
Provided the objective function and constraints are sufficiently well behaved, if a local minimizer is used in conjunction with e05sbc, then it is more likely that the final result will at least be in the near vicinity of a local optimum, and due to the global search characteristics of the particle swarm, this solution should be superior to many other local optima.
Caution should be used in accelerating the rate of convergence, as with faster convergence, less of the domain will remain searchable by the swarm, making it increasingly difficult for the algorithm to detect the basins of attraction of superior local optima. Using the options
${\mathbf{Repulsion\; Initialize}}$ and
${\mathbf{Repulsion\; Finalize}}$ described in
Section 12 will help to overcome this, by causing the swarm to diverge away from the current optimum once no more local improvement is likely.
On successful exit with guaranteed success,
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR (or
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_FAST_SOLUTION). This may happen if a
${\mathbf{Target\; Objective\; Value}}$ is assigned and is reached by the algorithm at a satisfactorily constrained point. It will also occur if a constrained point is found when
${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$ is set.
On successful exit without guaranteed success,
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED is returned. This will happen if another finalization criterion is achieved without the detection of an error.
In both cases, the value of
inform provides further information as to the cause of the exit.
8
Parallelism and Performance
The code for e05sbc is not directly threaded for parallel execution. In particular, none of the usersupplied functions will be called from within a parallel region generated by e05sbc.
The memory used by e05sbc is relatively static throughout. Indeed, most of the memory required is used to store the current particle locations, the cognitive particle memories, the particle velocities and the particle weights. As such, e05sbc may be used in problems with high dimension number (${\mathbf{ndim}}>100$) without the concern of computational resource exhaustion, although the probability of successfully locating the global optimum will decrease dramatically with the increase in dimensionality.
Due to the stochastic nature of the algorithm, the result will vary over multiple runs. This is particularly true if arguments and options are chosen to accelerate convergence at the expense of the global search. However, the option ${\mathbf{Repeatability}}=\mathrm{ON}$ may be set to initialize the internal random number generator using a preset seed, which will result in identical solutions being obtained.
10
Example
This example uses a particle swarm to find the global minimum of the twodimensional Schwefel function:
subject to the constraints:
The global optimum has an objective value of ${f}_{\mathrm{min}}=731.707$, located at $\mathbf{x}=\left(394.15,433.48\right)$. Only the third constraint is active at this point.
The example demonstrates how to initialize and set the options arrays using
e05zkc, how to query options using
e05zlc, and finally how to search for the global optimum using
e05sbc. The problem is solved twice, first using
e05sbc alone, and secondly by coupling
e05sbc with
e04ucc as a dedicated local minimizer. In both cases the default option
${\mathbf{Repeatability}}=\mathrm{ON}$ is used to produce repeatable solutions.
10.1
Program Text
10.2
Program Data
None.
10.3
Program Results
11
Algorithmic Details
The following pseudocode describes the algorithm used with the repulsion mechanism.
The definition of terms used in the above pseudocode are as follows.
$\mathit{npar}$

the number of particles, npar 
${\mathbf{\ell}}_{\mathrm{box}}$ 
array of ndim lower box bounds 
${\mathbf{u}}_{\mathrm{box}}$ 
array of ndim upper box bounds 
${\mathbf{x}}_{j}$ 
position of particle $j$ 
${\hat{\mathbf{x}}}_{j}$ 
best position found by particle $j$ 
$\stackrel{~}{\mathbf{x}}$ 
best position found by any particle 
${f}_{j}$ 
$F\left({\mathbf{x}}_{j}\right)$ 
${\hat{f}}_{j}$ 
$F\left({\hat{\mathbf{x}}}_{j}\right)$, best value found by particle $j$ 
$\stackrel{~}{f}$ 
$F\left(\stackrel{~}{\mathbf{x}}\right)$, best value found by any particle 
${e}_{k}\left(\mathbf{x}\right)$ 
$k$th (scaled) constraint violation at $\mathbf{x}$, evaluated as $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({c}_{k}\left(\mathbf{x}\right){l}_{{\mathbf{ndim}}+k},0.0\right)+\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({c}_{k}\left(\mathbf{x}\right){u}_{{\mathbf{ndim}}+k},0.0\right)$; this may be scaled by the maximum $k$th constraint found thus far 
$\mathbf{e}\left(\mathbf{x}\right)$ 
the array of ncon constraint violations, ${e}_{\mathit{k}}\left(\mathbf{x}\right)$, for $\mathit{k}=1,2,\dots ,{\mathbf{ncon}}$, at a point $\mathbf{x}$ 
${\mathbf{e}}_{j}$ 
$\mathbf{e}\left({\mathbf{x}}_{j}\right)$, the array of constraint violations evaluated at ${\mathbf{x}}_{j}$ 
${\hat{\mathbf{e}}}_{j}$ 
$\mathbf{e}\left({\hat{\mathbf{x}}}_{j}\right)$, the array of constraint violations evaluated at ${\hat{\mathbf{x}}}_{j}$ 
$\stackrel{~}{\mathbf{e}}$ 
$\mathbf{e}\left(\stackrel{~}{\mathbf{x}}\right)$, the array of constraint violations evaluated at $\stackrel{~}{\mathbf{x}}$ 
${\mathbf{v}}_{j}$ 
velocity of particle $j$ 
${w}_{j}$ 
weight on ${\mathbf{v}}_{j}$ for velocity update, decreasing according to ${\mathbf{Weight\; Decrease}}$ 
${\mathbf{V}}_{\mathrm{max}}$ 
maximum absolute velocity, dependent upon ${\mathbf{Maximum\; Variable\; Velocity}}$ 
${I}_{c}$ 
swarm iteration counter 
${I}_{s}$ 
iterations since $\stackrel{~}{\mathbf{x}}$ was updated 
${f}_{\mathrm{scale}}$ 
objective function scaling defined by the options ${\mathbf{Constraint\; Scaling}}$, ${\mathbf{Objective\; Scaling}}$ and ${\mathbf{Objective\; Scale}}$. 
${\mathbf{D}}_{1},{\mathbf{D}}_{2}$ 
diagonal matrices with random elements in range $\left(0,1\right)$ 
${C}_{s}$ 
the cognitive advance coefficient which weights velocity towards ${\hat{\mathbf{x}}}_{j}$, adjusted using ${\mathbf{Advance\; Cognitive}}$ 
${C}_{g}$ 
the global advance coefficient which weights velocity towards $\stackrel{~}{\mathbf{x}}$, adjusted using ${\mathbf{Advance\; Global}}$ 
$\mathit{dtol}$ 
the ${\mathbf{Distance\; Tolerance}}$ for resetting a converged particle 
$\mathbf{R}\in U\left({\mathbf{\ell}}_{\mathrm{box}},{\mathbf{u}}_{\mathrm{box}}\right)$ 
an array of random numbers whose $i$th element is drawn from a uniform distribution in the range $\left({{\mathbf{\ell}}_{\mathrm{box}}}_{\mathit{i}},{{\mathbf{u}}_{\mathrm{box}}}_{\mathit{i}}\right)$, for $\mathit{i}=1,2,\dots ,{\mathbf{ndim}}$ 
${O}_{i}$ 
local optimizer interior options 
${O}_{e}$ 
local optimizer exterior options 
$\varphi \left({w}_{j}\right)$ 
a function of ${w}_{j}$ designed to increasingly weight towards minimizing constraint violations as ${w}_{j}$ decreases 
$\mathrm{LOCMIN}\left(\mathbf{x},f,\mathbf{e},O\right)$ 
apply local optimizer using the set of options $O$ using the solution $\left(\mathbf{x},f,\mathbf{e}\right)$ as the starting point, if used (not default) 
monmod 
monitor progress and possibly modify ${\mathbf{x}}_{j}$ 
BOUNDARY 
apply required behaviour for ${\mathbf{x}}_{j}$ outside bounding box, (see ${\mathbf{Boundary}}$) 
new ($\stackrel{~}{f}$) 
true if $\stackrel{~}{\mathbf{x}}$, $\stackrel{~}{\mathbf{c}}$, $\stackrel{~}{f}$ were updated at this iteration 
Additionally a repulsion phase can be introduced by changing from the default values of options
${\mathbf{Repulsion\; Finalize}}$ (
${r}_{f}$),
${\mathbf{Repulsion\; Initialize}}$ (
${r}_{i}$) and
${\mathbf{Repulsion\; Particles}}$ (
${r}_{p}$). If the number of static particles is denoted
${n}_{s}$ then the following can be inserted after the new(
$\stackrel{~}{f}$) check in the pseudocode above.
12
Optional Parameters
This section can be skipped if you wish to use the default values for all optional parameters, otherwise, the following is a list of the optional parameters available and a full description of each optional parameter is provided in
Section 12.1.
12.1
Description of the Optional Parameters
For each option, we give a summary line, a description of the optional parameter and details of constraints.
The summary line contains:
 the keywords;
 a parameter value,
where the letters $a$, $i$ and $r$ denote options that take character, integer and real values respectively;
 the default value, where the symbol $\epsilon $ is a generic notation for machine precision (see X02AJC), and $\mathit{Imax}$ represents the largest representable integer value (see X02BBC).
All options accept the value ‘DEFAULT’ in order to return single options to their default states.
Keywords and character values are case insensitive, however they must be separated by at least one space.
For
e05sbc the maximum length of the argument
cvalue used by
e05zlc is
$15$.
Advance Cognitive  $r$  Default $\text{}=2.0$ 
The cognitive advance coefficient, ${C}_{s}$. When larger than the global advance coefficient, this will cause particles to be attracted toward their previous best positions. Setting $r=0.0$ will cause e05sbc to act predominantly as a local optimizer. Setting $r>2.0$ may cause the swarm to diverge, and is generally inadvisable. At least one of the global and cognitive coefficients must be nonzero.
Advance Global  $r$  Default $\text{}=2.0$ 
The global advance coefficient, ${C}_{g}$. When larger than the cognitive coefficient this will encourage convergence toward the best solution yet found. Values $r\in \left(0,1\right)$ will inhibit particles overshooting the optimum. Values $r\in \left[1,2\right)$ cause particles to fly over the optimum some of the time. Larger values can prohibit convergence. Setting $r=0.0$ will remove any attraction to the current optimum, effectively generating a Monte Carlo multistart optimization algorithm. At least one of the global and cognitive coefficients must be nonzero.
Boundary  $a$  Default $\text{}=\mathrm{FLOATING}$ 
Determines the behaviour if particles leave the domain described by the box bounds. This only affects the general PSO algorithm, and will not pass down to any NAG local minimizers chosen.
This option is only effective in those dimensions for which ${\mathbf{bl}}\left[i1\right]\ne {\mathbf{bu}}\left[i1\right]$, $i=1,2,\dots ,{\mathbf{ndim}}$.
 IGNORE
 The box bounds are ignored. The objective function is still evaluated at the new particle position.
 RESET
 The particle is reinitialized inside the domain. ${\hat{\mathbf{x}}}_{j}$, ${\hat{f}}_{j}$ and ${\hat{\mathbf{e}}}_{j}$ are not affected.
 FLOATING
 The particle position remains the same, however the objective function will not be evaluated at the next iteration. The particle will probably be advected back into the domain at the next advance due to attraction by the cognitive and global memory.
 HYPERSPHERICAL
 The box bounds are wrapped around an $\mathit{ndim}$dimensional hypersphere. As such a particle leaving through a lower bound will immediately reenter through the corresponding upper bound and vice versa. The standard distance between particles is also modified accordingly.
 FIXED
 The particle rests on the boundary, with the corresponding dimensional velocity set to $0.0$.
Constraint Norm  $a$  Default $\text{}=\mathrm{L1}$ 
Determines with respect to which norm the constraint residuals should be constructed. These are automatically scaled with respect to
ncon as stated. For the set of (scaled) violations
$\mathbf{e}$, these may be,
 L1
 The ${L}^{1}$ norm will be used, ${\Vert \mathbf{e}\Vert}_{1}=\frac{1}{{\mathbf{ncon}}}{\displaystyle \sum _{1}^{{\mathbf{ncon}}}}\left{e}_{k}\right$
 L2
 The ${L}^{2}$ norm will be used, ${\Vert \mathbf{e}\Vert}_{2}=\frac{1}{{\mathbf{ncon}}}\sqrt{{\displaystyle \sum _{1}^{{\mathbf{ncon}}}}{e}_{k}^{2}}$
 L2SQ
 The square of the ${L}^{2}$ norm will be used, ${\Vert \mathbf{e}\Vert}_{{2}^{2}}=\frac{1}{{\mathbf{ncon}}}{\displaystyle \sum _{1}^{{\mathbf{ncon}}}}{e}_{k}^{2}$
 LMAX
 The ${L}^{\infty}$ norm will be used, ${\Vert \mathbf{e}\Vert}_{\infty}={\displaystyle \underset{0<k\le {\mathbf{ncon}}}{\mathrm{max}}}\phantom{\rule{0.25em}{0ex}}\left(\left{e}_{k}\right\right)$
Constraint Scale Maximum  $r$  Default $\text{}=\text{1.0e6}$ 
Internally, each constraint violation is scaled with respect to the maximum violation yet achieved for that constraint. This option acts as a ceiling for this scale.
Constraint:
$r>1.0$.
Constraint Scaling  $a$  Default $\text{}=\mathrm{INITIAL}$ 
Determines whether to scale the constraints and objective function when constructing the penalty function.
 OFF
 Neither the constraint violations nor the objective will be scaled automatically. This should only be used if the constraints and objective are similarly scaled everywhere throughout the domain.
 INITIAL
 The maximum of the initial cognitive memories, ${\hat{f}}_{j}$ and ${\hat{\mathbf{e}}}_{j}$, will be used to scale the objective function and constraint violations respectively.
 ADAPTIVE
 Initially, the maximum of the initial cognitive memories, ${\hat{f}}_{j}$ and ${\hat{\mathbf{e}}}_{j}$, will be used to scale the objective function and constraint violations respectively. If a significant change is detected in the behaviour of the constraints or the objective, these will be rescaled with respect to the current state of the cognitive memory.
Constraint Superiority  $r$  Default $\text{}=0.01$ 
The minimum scaled improvement in the constraint violation for a location to be immediately superior to that in memory, regardless of the objective value.
Constraint:
$r>0.0$.
Constraint Tolerance  $r$  Default $\text{}={10}^{4}$ 
The maximum scaled violation of the constraints for which a sample particle is considered comparable to the current global optimum. Should this not be exceeded, then the current global optimum will be updated if the value of the objective function of the sample particle is superior.
Constraint Warning  $a$  Default $\text{}=\mathrm{ON}$ 
Activates or deactivates the error exit associated with the inability to completely satisfy all constraints,
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_NOT_FEASIBLE. It is advisable to deactivate this option if the exit
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_NOT_FEASIBLE is preferred in such cases.
 OFF
 ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_NOT_FEASIBLE will not be returned.
 ON
 ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_NOT_FEASIBLE will be returned if any constraints are sufficiently violated at the end of the simulation.
Distance Scaling  $a$  Default $\text{}=\mathrm{ON}$ 
Determines whether distances should be scaled by box widths.
 ON
 When a distance is calculated between $\mathbf{x}$ and $\mathbf{y}$, a scaled ${L}^{2}$ norm is used.
 OFF
 Distances are calculated as the standard ${L}^{2}$ norm without any rescaling.
Distance Tolerance  $r$  Default $\text{}={10}^{4}$ 
This is the distance, $\mathit{dtol}$ between particles and the global optimum which must be reached for the particle to be considered converged, i.e., that any subsequent movement of such a particle cannot significantly alter the global optimum. Once achieved the particle is reset into the box bounds to continue searching.
Constraint:
$r>0.0$.
Function Precision  $r$  Default $\text{}={\epsilon}^{0.9}$ 
The parameter defines ${\epsilon}_{r}$, which is intended to be a measure of the accuracy with which the problem function $F\left(\mathbf{x}\right)$ can be computed. If $r<\epsilon $ or $r\ge 1$, the default value is used.
The value of
${\epsilon}_{r}$ should reflect the relative precision of
$1+\leftF\left(\mathbf{x}\right)\right$; i.e.,
${\epsilon}_{r}$ acts as a relative precision when
$\leftF\right$ is large, and as an absolute precision when
$\leftF\right$ is small. For example, if
$F\left(\mathbf{x}\right)$ is typically of order
$1000$ and the first six significant digits are known to be correct, an appropriate value for
${\epsilon}_{r}$ would be
${10}^{6}$. In contrast, if
$F\left(\mathbf{x}\right)$ is typically of order
${10}^{4}$ and the first six significant digits are known to be correct, an appropriate value for
${\epsilon}_{r}$ would be
${10}^{10}$. The choice of
${\epsilon}_{r}$ can be quite complicated for badly scaled problems; see Chapter 8 of
Gill et al. (1981) for a discussion of scaling techniques. The default value is appropriate for most simple functions that are computed with full accuracy. However when the accuracy of the computed function values is known to be significantly worse than full precision, the value of
${\epsilon}_{r}$ should be large enough so that no attempt will be made to distinguish between function values that differ by less than the error inherent in the calculation.
Local Boundary Restriction  $r$  Default $\text{}=0.5$ 
Contracts the box boundaries used by a box constrained local minimizer to,
$\left[{\beta}_{l},{\beta}_{u}\right]$, containing the start point
$x$, where
Smaller values of
$r$ thereby restrict the size of the domain exposed to the local minimizer, possibly reducing the amount of work done by the local minimizer.
Constraint:
$0.0\le r\le 1.0$.
Local Interior Iterations  ${i}_{1}$  
Local Interior Major Iterations  ${i}_{1}$  
Local Exterior Iterations  ${i}_{2}$  
Local Exterior Major Iterations  ${i}_{2}$  
The maximum number of iterations or function evaluations the chosen local minimizer will perform inside (outside) the main loop if applicable. For the NAG minimizers these correspond to:
Minimizer 
Parameter/option 
Default Interior 
Default Exterior 
e04cbc 
maxcal 
${\mathbf{ndim}}+10$ 
$2\times {\mathbf{ndim}}+15$ 
e04dgc 
${\mathbf{Iteration\; Limit}}$ 
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(30,3\times {\mathbf{ndim}}\right)$ 
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(50,5\times {\mathbf{ndim}}\right)$ 
e04ucc 
${\mathbf{Major\; Iteration\; Limit}}$ 
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(10,2\times {\mathbf{ndim}}\right)$ 
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(30,3\times {\mathbf{ndim}}\right)$ 
Unless set, these are functions of the parameters passed to e05sbc.
Setting $i=0$ will disable the local minimizer in the corresponding algorithmic region. For example, setting ${\mathbf{Local\; Interior\; Iterations}}=0$ and ${\mathbf{Local\; Exterior\; Iterations}}=30$ will cause the algorithm to perform no local minimizations inside the main loop of the algorithm, and a local minimization with upto $30$ iterations after the main loop has been exited.
Constraint:
${i}_{1}\ge 0$, ${i}_{2}\ge 0$.
Local Interior Tolerance  ${r}_{1}$  Default $\text{}={10}^{4}$ 
Local Exterior Tolerance  ${r}_{2}$  Default $\text{}={10}^{4}$ 
This is the tolerance provided to a local minimizer in the interior (exterior) of the main loop of the algorithm.
Constraint:
${r}_{1}>0.0$,${r}_{2}>0.0$.
Local Interior Minor Iterations  ${i}_{1}$  
Local Exterior Minor Iterations  ${i}_{2}$  
Where applicable, the secondary number of iterations the chosen local minimizer will use inside (outside) the main loop. Currently the relevant default values are:
Minimizer 
Parameter/option 
Default Interior 
Default Exterior 
e04ucc 
${\mathbf{Minor\; Iteration\; Limit}}$ 
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(10,2\times {\mathbf{ndim}}\right)$ 
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(30,3\times {\mathbf{ndim}}\right)$ 
Constraint:
${i}_{1}\ge 0$, ${i}_{2}\ge 0$.
Local Minimizer  $a$  Default $\text{}=\mathrm{OFF}$ 
Allows for a choice of
Chapter E04 functions to be used as a coupled, dedicated local minimizer.
 ${\mathbf{Local\; Minimizer}}=\mathrm{OFF}$
 No local minimization will be performed in either the INTERIOR or EXTERIOR sections of the algorithm.
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04cbc}$
 Use e04cbc as the local minimizer. This does not require the calculation of derivatives.
On a call to
objfun during a local minimization,
${\mathbf{mode}}=5$.
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04dgc}$
 Use e04dgc as the local minimizer.
Accurate derivatives must be provided, and will not be approximated internally. Additionally, each call to
objfun during a local minimization will require either the objective to be evaluated alone, or both the objective and its gradient to be evaluated. Hence on a call to
objfun,
${\mathbf{mode}}=5$ or
$7$.
 ${\mathbf{Local\; Minimizer}}=\mathrm{e04ucc}$
 Use e04ucc as the local minimizer.
This operates such that any derivatives of either the objective function or the constraint Jacobian, which you cannot supply, will be approximated internally using finite differences.
Either, the objective, objective gradient, or both may be requested during a local minimization, and as such on a call to
objfun,
${\mathbf{mode}}=1$,
$2$ or
$5$.
The box bounds forwarded to this function from e05sbc will have been acted upon by ${\mathbf{Local\; Boundary\; Restriction}}$. As such, the domain exposed may be greatly smaller than that provided to e05sbc.
Maximum Function Evaluations  $i$  Default $=\mathit{Imax}$ 
The maximum number of evaluations of the objective function. When reached this will return
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED and
${\mathbf{inform}}=6$.
Constraint:
$i>0$.
Maximum Iterations Completed  $i$  Default $\text{}=1000\times {\mathbf{ndim}}$ 
The maximum number of complete iterations that may be performed. Once exceeded
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED and
${\mathbf{inform}}=5$.
Unless set, this adapts to the parameters passed to e05sbc.
Constraint:
$i\ge 1$.
Maximum Iterations Static  $i$  Default $\text{}=100$ 
The maximum number of iterations without any improvement to the current global optimum. If exceeded
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED and
${\mathbf{inform}}=4$. This exit will be hindered by setting
${\mathbf{Maximum\; Iterations\; Static\; Particles}}$ to larger values.
Constraint:
$i\ge 1$.
Maximum Iterations Static Particles  $i$  Default $\text{}=0$ 
The minimum number of particles that must have converged to the current optimum before the function may exit due to
${\mathbf{Maximum\; Iterations\; Static}}$ with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED and
${\mathbf{inform}}=4$.
Constraint:
$i\ge 0$.
Maximum Particles Converged  $i$  Default $=\mathit{Imax}$ 
The maximum number of particles that may converge to the current optimum. When achieved,
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED and
${\mathbf{inform}}=3$. This exit will be hindered by setting ‘
Repulsion’ options, as these cause the swarm to reexpand.
Constraint:
$i>0$.
Maximum Particles Reset  $i$  Default $=\mathit{Imax}$ 
The maximum number of particles that may be reset after converging to the current optimum. Once achieved no further particles will be reset, and any particles within ${\mathbf{Distance\; Tolerance}}$ of the global optimum will continue to evolve as normal.
Constraint:
$i>0$.
Maximum Variable Velocity  $r$  Default $\text{}=0.25$ 
Along any dimension $j$, the absolute velocity is bounded above by $\left{\mathbf{v}}_{j}\right\le r\times \left({\mathbf{u}}_{j}{\mathbf{\ell}}_{j}\right)={\mathbf{V}}_{\mathrm{max}}$. Very low values will greatly increase convergence time. There is no upper limit, although larger values will allow more particles to be advected out of the box bounds, and values greater than $4.0$ may cause significant and potentially unrecoverable swarm divergence.
Constraint:
$r>0.0$.
Objective Scale  $r$  Default $\text{}=1.0$ 
The initial scale for the objective function. This will remain fixed if ${\mathbf{Objective\; Scaling}}=\mathrm{USER}$ is selected.
Objective Scaling  $a$  Default $\text{}=\mathrm{MAXIMUM}$ 
The method of (re)scaling applied to the objective function when the function detects a significant difference between the scale and the global and cognitive memory ($\stackrel{~}{f}$ and ${\hat{f}}_{j}$). This only has an effect when ${\mathbf{ncon}}>0$ and ${\mathbf{Constraint\; Scaling}}$ is active.
 MAXIMUM
 The objective is rescaled with respect to the maximum absolute value of the objective in the cognitive and global memory.
 MEAN
 The objective is rescaled with respect to the mean absolute value of the objective in the cognitive and global memory.
 USER
 The scale remains fixed at the value set using ${\mathbf{Objective\; Scale}}$.
Optimize  $a$  Default $\text{}=\mathrm{MINIMIZE}$ 
Determines whether to maximize or minimize the objective function, or ignore the objective and search for a constrained point.
 MINIMIZE
 The objective function will be minimized.
 MAXIMIZE
 The objective function will be maximized. This is accomplished by minimizing the negative of the objective.
 CONSTRAINTS
 The objective function will be ignored, and the algorithm will attempt to find a feasible point given the provided constraints. The objective function will be evaluated at the best point found with regards to constraint violations, and the final positions returned in xbest. The objective will be calculated at the best point found in terms of constraints only. Should a constrained point be found, e05sbc will exit with ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR and ${\mathbf{inform}}=6$.
Constraint:
if ${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$, ${\mathbf{ncon}}>0$ is required.
Repeatability  $a$  Default $\text{}=\mathrm{OFF}$ 
Allows for the same random number generator seed to be used for every call to
e05sbc.
${\mathbf{Repeatability}}=\mathrm{OFF}$ is recommended in general.
 OFF
 The internal generation of random numbers will be nonrepeatable.
 ON
 The same seed will be used.
Repulsion Finalize  $i$  Default $=\mathit{Imax}$ 
The number of iterations performed in a repulsive phase before recontraction. This allows a rediversified swarm to contract back toward the current optimum, allowing for a finer search of the near optimum space.
Constraint:
$i\ge 2$.
Repulsion Initialize  $i$  Default $=\mathit{Imax}$ 
The number of iterations without any improvement to the global optimum before the algorithm begins a repulsive phase. This phase allows the particle swarm to reexpand away from the current optimum, allowing more of the domain to be investigated. The repulsive phase is automatically ended if a superior optimum is found.
Constraint:
$i\ge 2$.
Repulsion Particles  $i$  Default $\text{}=0$ 
The number of particles required to have converged to the current optimum before any repulsive phase may be initialized. This will prevent repulsion before a satisfactory search of the near optimum area has been performed, which may happen for large dimensional problems.
Constraint:
$i\ge 0$.
Seed  $i$  Default $\text{}=0$ 
Sets the random number generator seed to be used when ${\mathbf{Repeatability}}=\mathrm{ON}$. If set to 0, the default seed will be used. If not, the absolute value of ${\mathbf{Seed}}$ will be used to generate the random number generator seed.
Start  $a$  Default $\text{}=\mathrm{COLD}$ 
Used to affect the initialization of the function.
 COLD
 The random number generators and all initialization data will be generated internally. The variables xbest, fbest and cbest need not be set.
 WARM
 You must supply the initial best location, function and constraint violation values xbest, fbest and cbest. This option is recommended if you already have a data set you wish to improve upon.
Swarm Standard Deviation  $r$  Default $\text{}=0.1$ 
The target standard deviation of the particle distances from the current optimum. Once the standard deviation is below this level,
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_SOLUTION_NOT_GUARANTEED and
${\mathbf{inform}}=2$. This criterion will be penalized by the use of ‘
Repulsion’ options, as these cause the swarm to reexpand, increasing the standard deviation of the particle distances from the best point.
Constraint:
$r\ge 0.0$.
Target Objective  $a$  Default $\text{}=\mathrm{OFF}$ 
Target Objective Value  $r$  Default $\text{}=0.0$ 
Activate or deactivate the use of a target value as a finalization criterion. If active, then once the supplied target value for the objective function is found (beyond the first iteration if
${\mathbf{Target\; Warning}}$ is active)
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR and
${\mathbf{inform}}=1$. Other than checking for feasibility only (
${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$), this is the only finalization criterion that guarantees that the algorithm has been successful. If the target value was achieved at the initialization phase or first iteration and
${\mathbf{Target\; Warning}}$ is active,
e05sbc will exit with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_FAST_SOLUTION. This option may take any real value
$r$, or the character ON/OFF as well as DEFAULT. If this option is queried using
e05zlc, the current value of
$r$ will be returned in
rvalue, and
cvalue will indicate whether this option is ON or OFF. The behaviour of the option is as follows:
 $r$
 Once a point is found with an objective value within the ${\mathbf{Target\; Objective\; Tolerance}}$ of $r$, e05sbc will exit successfully with ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR and ${\mathbf{inform}}=1$.
 OFF
 The current value of $r$ will remain stored, however it will not be used as a finalization criterion.
 ON
 The current value of $r$ stored will be used as a finalization criterion.
 DEFAULT
 The stored value of $r$ will be reset to its default value ($0.0$), and this finalization criterion will be deactivated.
Target Objective Safeguard  $r$  Default $\text{}=10.0\epsilon $ 
If you have given a target objective value to reach in $\mathit{objval}$ (the value of the optional parameter ${\mathbf{Target\; Objective\; Value}}$), $\mathit{objsfg}$ sets your desired safeguarded termination tolerance, for when $\mathit{objval}$ is close to zero.
Constraint:
$\mathit{objsfg}\ge 2\epsilon $.
Target Objective Tolerance  $r$  Default $\text{}=0.0$ 
The optional tolerance to a userspecified target value.
Constraint:
$r\ge 0.0$.
Target Warning  $a$  Default $\text{}=\mathrm{OFF}$ 
Activates or deactivates the error exit associated with the target value being achieved before entry into the main loop of the algorithm,
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_FAST_SOLUTION.
 OFF
 No error will be returned, and the function will exit normally.
 ON
 An error will be returned if the target objective is reached prematurely, and the function will exit with ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NW_FAST_SOLUTION.
Verify Gradients  $a$  Default $\text{}=\mathrm{ON}$ 
Adjusts the level of gradient checking performed when gradients are required. Gradient checks are only performed on the first call to the chosen local minimizer if it requires gradients. There is no guarantee that the gradient check will be correct, as the finite differences used in the gradient check are themselves subject to inaccuracies.
 OFF
 No gradient checking will be performed.
 ON
 A cheap gradient check will be performed on both the gradients corresponding to the objective through objfun and those provided via the constraint Jacobian through confun.
 OBJECTIVE
 A more expensive gradient check will be performed on the gradients corresponding to the objective objfun. The gradients of the constraints will not be checked.
 CONSTRAINTS
 A more expensive check will be performed on the elements of cjac provided via confun. The objective gradient will not be checked.
 FULL
 A more expensive check will be performed on both the gradient of the objective and the constraint Jacobian.
Weight Decrease  $a$  Default $\text{}=\mathrm{INTEREST}$ 
Determines how particle weights decrease.
 OFF
 Weights do not decrease.
 INTEREST
 Weights decrease through compound interest as ${w}_{\mathit{IT}+1}={w}_{\mathit{IT}}\left(1{W}_{\mathit{val}}\right)$, where ${W}_{\mathit{val}}$ is the ${\mathbf{Weight\; Value}}$ and $\mathit{IT}$ is the current number of iterations.
 LINEAR
 Weights decrease linearly following ${w}_{\mathit{IT}+1}={w}_{\mathit{IT}}\mathit{IT}\times \left({W}_{\mathit{max}}{W}_{\mathit{min}}\right)/{\mathit{IT}}_{\mathit{max}}$, where $\mathit{IT}$ is the iteration number and ${\mathit{IT}}_{\mathit{max}}$ is the maximum number of iterations as set by ${\mathbf{Maximum\; Iterations\; Completed}}$.
Weight Initial  $r$  Default $\text{}={W}_{\mathit{max}}$ 
The initial value of any particle's inertial weight, ${W}_{\mathit{ini}}$, or the minimum possible initial value if initial weights are randomized. When set, this will override ${\mathbf{Weight\; Initialize}}=\mathrm{RANDOMIZED}$ or $\mathrm{MAXIMUM}$, and as such these must be set afterwards if so desired.
Constraint:
${W}_{\mathit{min}}\le r\le {W}_{\mathit{max}}$.
Weight Initialize  $a$  Default $\text{}=\mathrm{MAXIMUM}$ 
Determines how the initial weights are distributed.
 INITIAL
 All weights are initialized at the initial weight, ${W}_{\mathit{ini}}$, if set. If ${\mathbf{Weight\; Initial}}$ has not been set, this will be the maximum weight, ${W}_{\mathit{max}}$.
 MAXIMUM
 All weights are initialized at the maximum weight, ${W}_{\mathit{max}}$.
 RANDOMIZED
 Weights are uniformly distributed in $\left({W}_{\mathit{min}},{W}_{\mathit{max}}\right)$ or $\left({W}_{\mathit{ini}},{W}_{\mathit{max}}\right)$ if ${\mathbf{Weight\; Initial}}$ has been set.
Weight Maximum  $r$  Default $\text{}=1.0$ 
The maximum particle weight, ${W}_{\mathit{max}}$.
Constraint:
$1.0\ge r\ge {W}_{\mathit{min}}$ (If ${W}_{\mathit{ini}}$ has been set then $1.0\ge r\ge {W}_{\mathit{ini}}$.)
Weight Minimum  $r$  Default $\text{}=0.1$ 
The minimum achievable weight of any particle, ${W}_{\mathit{min}}$. Once achieved, no further weight reduction is possible.
Constraint:
$0.0\le r\le {W}_{\mathit{max}}$ (If ${W}_{\mathit{ini}}$ has been set then $0.0\le r\le {W}_{\mathit{ini}}$.)
Weight Reset  $a$  Default $\text{}=\mathrm{MAXIMUM}$ 
Determines how particle weights are reinitialized.
 INITIAL
 Weights are reinitialized at the initial weight if set. If ${\mathbf{Weight\; Initial}}$ has not been set, this will be the maximum weight.
 MAXIMUM
 Weights are reinitialized at the maximum weight.
 RANDOMIZED
 Weights are uniformly distributed in $\left({W}_{\mathit{min}},{W}_{\mathit{max}}\right)$ or $\left({W}_{\mathit{ini}},{W}_{\mathit{max}}\right)$ if ${\mathbf{Weight\; Initial}}$ has been set.
Weight Value  $r$  Default $\text{}=0.01$ 
The constant ${W}_{\mathit{val}}$ used with ${\mathbf{Weight\; Decrease}}=\mathrm{INTEREST}$.
Constraint:
$0.0\le r\le \frac{1}{3}$.