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NAG Toolbox: nag_glopt_bnd_pso (e05sa)
Purpose
nag_glopt_bnd_pso (e05sa) is designed to search for the global minimum or maximum of an arbitrary function, using Particle Swarm Optimization (PSO). Derivatives are not required, although these may be used by an accompanying local minimization function if desired.
nag_glopt_bnd_pso (e05sa) is essentially identical to
nag_glopt_nlp_pso (e05sb), but with a simpler interface and with various optional parameters removed; otherwise most parameters are identical. In particular,
nag_glopt_bnd_pso (e05sa) does not handle general constraints.
Information for users of the NAG Toolbox
nag_glopt_bnd_pso (e05sa) has been designed to be particularly effective on SMP systems, by allowing multiple threads to advance subiterations of the algorithm in a highly asynchronous manner. In doing this, the callback functions supplied to
nag_glopt_bnd_pso (e05sa) are called simultaneously on multiple threads, and therefore must themselves be thread safe. In particular, the arrays
user and
user provided are classified as OpenMP shared memory, and as such it is imperative that any operations performed on these arrays are done in an appropriate manner. Failure to ensure thread safety of the provided callback functions may result in unpredictable behaviour, including the callback functions returning completely wrong solutions to
nag_glopt_bnd_pso (e05sa) – invalidating any solution returned.
Syntax
[
xb,
fb,
iopts,
opts,
user,
itt,
inform,
ifail] = e05sa(
bl,
bu,
objfun,
monmod,
iopts,
opts, 'ndim',
ndim, 'npar',
npar, 'user',
user)
[
xb,
fb,
iopts,
opts,
user,
itt,
inform,
ifail] = nag_glopt_bnd_pso(
bl,
bu,
objfun,
monmod,
iopts,
opts, 'ndim',
ndim, 'npar',
npar, 'user',
user)
Before calling
nag_glopt_bnd_pso (e05sa),
nag_glopt_optset (e05zk) must be called with
optstr set to ‘
Initialize = e05sac’. Optional parameters may also be specified by calling
nag_glopt_optset (e05zk) before the call to
nag_glopt_bnd_pso (e05sa).
Note: the interface to this routine has changed since earlier releases of the toolbox:
.
Description
nag_glopt_bnd_pso (e05sa) uses a stochastic method based on Particle Swarm Optimization (PSO) to search for the global optimum of a nonlinear function
F$F$, subject to a set of bound constraints on the variables. In the PSO algorithm (see
Section [Algorithmic Details]), 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(x)$F\left(\mathbf{x}\right)$ is a scalar function,
x$\mathbf{x}$ is a vector in
R^{ndim}${R}^{\mathit{ndim}}$ and the vectors
ℓ ≤ u$\mathbf{\ell}\le \mathbf{u}$ are lower and upper bounds respectively for the
ndim$\mathit{ndim}$ variables. The objective function may be nonlinear. Continuity of
F$F$ 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 nag_glopt_bnd_pso (e05sa).
For multimodal functions for which derivatives cannot be provided, particularly functions with a significant level of noise in their evaluation,
nag_glopt_bnd_pso (e05sa) should be used either alone, or coupled with
nag_opt_uncon_simplex (e04cb).
The
ndim$\mathit{ndim}$ lower and upper box bounds on the variable
x$\mathbf{x}$ are included to initialize the particle swarm into a finite hypervolume, although their subsequent influence on the algorithm is user determinable (see the option
Boundary in
Section [Optional Parameters]). It is strongly recommended that sensible bounds are provided for all variables.
nag_glopt_bnd_pso (e05sa) may also be used to maximize the objective function (see the option
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,
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$, 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 and
objfun.
nag_glopt_nlp_pso (e05sb) provides a comprehensive interface, allowing for the inclusion of general nonlinear constraints.
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
Parameters
Note: for descriptions of the symbolic variables, see
Section [Algorithmic Details].
Compulsory Input Parameters
 1:
bl(ndim) – double array
 2:
bu(ndim) – double array
ndim, the dimension of the array, must satisfy the constraint
ndim ≥ 1${\mathbf{ndim}}\ge 1$.
bl${\mathbf{bl}}$ is
ℓ$\mathbf{\ell}$, the array of lower bounds,
bu is
u$\mathbf{u}$, the array of upper bounds. The
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
Boundary in
Section [Optional Parameters]).
If
bl(i) = bu(i)${\mathbf{bl}}\left(i\right)={\mathbf{bu}}\left(i\right)$ for any
i ∈ {1, … ,ndim}$i\in \{1,\dots ,{\mathbf{ndim}}\}$, variable
i$i$ will remain locked to
bl(i)${\mathbf{bl}}\left(i\right)$ regardless of the
Boundary option selected.
It is strongly advised that you place sensible lower and upper bounds on all variables, even if your model allows for variables to be unbounded (using the option
Boundary = ignore${\mathbf{Boundary}}=\mathrm{ignore}$) since these define the initial search space.
Constraints:
 bl(i) ≤ bu(i)${\mathbf{bl}}\left(\mathit{i}\right)\le {\mathbf{bu}}\left(\mathit{i}\right)$, for i = 1,2, … ,ndim$\mathit{i}=1,2,\dots ,{\mathbf{ndim}}$;
 bl(i) ≠ bu(i)${\mathbf{bl}}\left(i\right)\ne {\mathbf{bu}}\left(i\right)$ for at least one i ∈ {1, … ,ndim}$i\in \{1,\dots ,{\mathbf{ndim}}\}$.
 3:
objfun – function handle or string containing name of mfile
objfun must, depending on the value of
mode, calculate the objective function
and/or calculate the gradient of the objective function for a
ndim$\mathit{ndim}$variable vector
x$\mathbf{x}$. Gradients are only required if a local minimizer has been chosen which requires gradients. See the option
Local Minimizer for more information.
[mode, objf, vecout, user] = objfun(mode, ndim, x, objf, vecout, nstate, user)
Input Parameters
 1:
mode – int64int32nag_int scalar
Indicates which functionality is required.
 mode = 0${\mathbf{mode}}=0$
 F(x)$F\left(\mathbf{x}\right)$ 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(x)$F\left(\mathbf{x}\right)$ that is greater than objf may be approximated by this upper bound; that is objf can remain unaltered.
 mode = 1${\mathbf{mode}}=1$
 Local Minimizer = 'e04uc'${\mathbf{Local\; Minimizer}}='\mathrm{e04uc}'$ only
First derivatives can be evaluated and returned in vecout. Any unaltered elements of vecout will be approximated using finite differences.
 mode = 2${\mathbf{mode}}=2$
 Local Minimizer = 'e04uc'${\mathbf{Local\; Minimizer}}='\mathrm{e04uc}'$ only
F(x)$F\left(\mathbf{x}\right)$ 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.
 mode = 5${\mathbf{mode}}=5$
 F(x)$F\left(\mathbf{x}\right)$ must be calculated and returned in objf. The value of objf on entry may not be used as an upper bound.
 mode = 6${\mathbf{mode}}=6$
 Local Minimizer = 'e04dg'${\mathbf{Local\; Minimizer}}='\mathrm{e04dg}'$ or 'e04kz'$'\mathrm{e04kz}'$ only
All first derivatives must be evaluated and returned in vecout.
 mode = 7${\mathbf{mode}}=7$
 Local Minimizer = 'e04dg'${\mathbf{Local\; Minimizer}}='\mathrm{e04dg}'$ or 'e04kz'$'\mathrm{e04kz}'$ only
F(x)$F\left(\mathbf{x}\right)$ must be calculated and returned in objf, and all first derivatives must be evaluated and returned in vecout.
 2:
ndim – int64int32nag_int scalar
The number of dimensions.
 3:
x(ndim) – double array
x$\mathbf{x}$, the point at which the objective function and/or its gradient are to be evaluated.
 4:
objf – double scalar
The value of
objf passed to
objfun varies with the parameter
mode.
 mode = 0${\mathbf{mode}}=0$
 objf is an upper bound for the value of F(x)$F\left(\mathbf{x}\right)$, often equal to the best value of F(x)$F\left(\mathbf{x}\right)$ found so far by a given particle. 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.
 mode = 1${\mathbf{mode}}=1$, 2$2$, 5$5$, 6$6$ or 7$7$
 objf is meaningless on entry.
 5:
vecout(ndim) – double array
If
Local Minimizer = e04uc${\mathbf{Local\; Minimizer}}=\mathrm{e04uc}$ or
e04uc$\mathrm{e04uc}$, the values of
vecout are used internally to indicate whether a finite difference approximation is required. See
nag_opt_nlp1_solve (e04uc).
 6:
nstate – int64int32nag_int scalar
nstate indicates various stages of initialization throughout the function. This allows for permanent global parameters to be initialized the least number of times. For example, you may initialize a random number generator seed.
 nstate = 3${\mathbf{nstate}}=3$
 SMP users only. objfun is called for the first time in a parallel region on a new thread other than the master thread. You may use this opportunity to set up any threaddependent information in user and user.
 nstate = 2${\mathbf{nstate}}=2$
 objfun is called for the very first time. You may save computational time if certain data must be read or calculated only once.
 nstate = 1${\mathbf{nstate}}=1$
 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.
 nstate = 0${\mathbf{nstate}}=0$
 Used in all other cases.
 7:
user – Any MATLAB object
objfun is called from
nag_glopt_bnd_pso (e05sa) with the object supplied to
nag_glopt_bnd_pso (e05sa).
Output Parameters
 1:
mode – int64int32nag_int scalar
If the value of
mode is set to be negative, then
nag_glopt_bnd_pso (e05sa) will exit as soon as possible with
ifail = 3${\mathbf{ifail}}={\mathbf{3}}$ and
inform = mode${\mathbf{inform}}={\mathbf{mode}}$.
 2:
objf – double scalar
The value of
objf returned varies with the parameter
mode.
 mode = 0${\mathbf{mode}}=0$
 objf must be the value of F(x)$F\left(\mathbf{x}\right)$. Only values of F(x)$F\left(\mathbf{x}\right)$ strictly less than objf on entry need be accurate.
 mode = 1${\mathbf{mode}}=1$ or 6$6$
 Need not be set.
 mode = 2${\mathbf{mode}}=2$, 5$5$ or 7$7$
 F(x)$F\left(\mathbf{x}\right)$ must be calculated and returned in objf. The entry value of objf may not be used as an upper bound.
 3:
vecout(ndim) – double array
The required values of
vecout returned to the calling function depend on the value of
mode.
 mode = 0${\mathbf{mode}}=0$ or 5$5$
 The value of vecout need not be set.
 mode = 1${\mathbf{mode}}=1$ or 2$2$
 vecout can contain components of the gradient of the objective function (∂F)/(∂x_{i})$\frac{\partial F}{\partial {x}_{i}}$ for some i = 1,2, … ndim$i=1,2,\dots {\mathbf{ndim}}$, or acceptable approximations. Any unaltered elements of vecout will be approximated using finite differences.
 mode = 6${\mathbf{mode}}=6$ or 7$7$
 vecout must contain the gradient of the objective function (∂F)/(∂x_{i})$\frac{\partial F}{\partial {x}_{i}}$ for all i = 1,2, … ndim$i=1,2,\dots {\mathbf{ndim}}$. Approximation of the gradient is strongly discouraged, and no finite difference approximations will be performed internally (see nag_opt_uncon_conjgrd_comp (e04dg) and nag_opt_bounds_mod_deriv_easy (e04kz)).
 4:
user – Any MATLAB object
 4:
monmod – function handle or string containing name of mfile
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 string
'e05sxm' .
[x, user, inform] = monmod(ndim, npar, x, xb, fb, xbest, fbest, itt, user, inform)
Input Parameters
 1:
ndim – int64int32nag_int scalar
The number of dimensions.
 2:
npar – int64int32nag_int scalar
The number of particles.
 3:
x(ndim,npar) – double array
Note: the
i$i$th component of the
j$j$th particle,
x_{j}(i)${x}_{j}\left(i\right)$, is stored in
x(i,j)${\mathbf{x}}\left(i,j\right)$.
The
npar particle locations,
x_{j}${\mathbf{x}}_{j}$, which will currently be used during the next iteration unless altered in
monmod.
 4:
xb(ndim) – double array
The location, x̃$\stackrel{~}{\mathbf{x}}$, of the best solution yet found.
 5:
fb – double scalar
The objective value, f̃ = F(x̃)$\stackrel{~}{f}=F\left(\stackrel{~}{\mathbf{x}}\right)$, of the best solution yet found.
 6:
xbest(ndim,npar) – double array
Note: the
i$i$th component of the position of the
j$j$th particle's cognitive memory,
x̂_{j}(i)${\hat{x}}_{j}\left(i\right)$, is stored in
xbest(i,j)${\mathbf{xbest}}\left(i,j\right)$.
The locations currently in the cognitive memory,
x̂_{j}${\hat{\mathbf{x}}}_{\mathit{j}}$, for
j = 1,2, … ,npar$\mathit{j}=1,2,\dots ,{\mathbf{npar}}$ (see
Section [Algorithmic Details]).
 7:
fbest(npar) – double array
The objective values currently in the cognitive memory,
F(x̂_{j})$F\left({\hat{\mathbf{x}}}_{\mathit{j}}\right)$, for
j = 1,2, … ,npar$\mathit{j}=1,2,\dots ,{\mathbf{npar}}$.
 8:
itt(6$6$) – int64int32nag_int array
Iteration and function evaluation counters (see description of
itt below).
 9:
user – Any MATLAB object
monmod is called from
nag_glopt_bnd_pso (e05sa) with the object supplied to
nag_glopt_bnd_pso (e05sa).
 10:
inform – int64int32nag_int scalar
inform = thread_num${\mathbf{inform}}=\mathbf{thread\_num}$, where
thread_num is the value returned by a call of the OpenMP function
OMP_GET_THREAD_NUM(). If running in serial this will always be zero.
Output Parameters
 1:
x(ndim,npar) – double array
Note: the
i$i$th component of the
j$j$th particle,
x_{j}(i)${x}_{j}\left(i\right)$, is stored in
x(i,j)${\mathbf{x}}\left(i,j\right)$.
The particle locations to be used during the next iteration.
 2:
user – Any MATLAB object
 3:
inform – int64int32nag_int scalar
Setting
inform < 0${\mathbf{inform}}<0$ will cause near immediate exit from
nag_glopt_bnd_pso (e05sa). This value will be returned as
inform with
ifail = 3${\mathbf{ifail}}={\mathbf{3}}$. You need not set
inform unless you wish to force an exit.
 5:
iopts( : $:$) – int64int32nag_int array
Optional parameter array as generated and possibly modified by calls to
nag_glopt_optset (e05zk). The contents of
iopts must not be modified directly between calls to
nag_glopt_bnd_pso (e05sa),
nag_glopt_optset (e05zk) or
nag_glopt_optget (e05zl).
Note: the contents of
iopts must not have been altered between calls to
nag_glopt_optset (e05zk),
nag_glopt_optget (e05zl),
nag_glopt_bnd_pso (e05sa) and the selected problem solving routine.
 6:
opts( : $:$) – double array
Optional parameter array as generated and possibly modified by calls to
nag_glopt_optset (e05zk). The contents of
opts must not be modified directly between calls to
nag_glopt_bnd_pso (e05sa),
nag_glopt_optset (e05zk) or
nag_glopt_optget (e05zl).
Note: the contents of
opts must not have been altered between calls to
nag_glopt_optset (e05zk),
nag_glopt_optget (e05zl),
nag_glopt_bnd_pso (e05sa) and the selected problem solving routine.
Optional Input Parameters
 1:
ndim – int64int32nag_int scalar
Default:
The dimension of the arrays
bl,
bu. (An error is raised if these dimensions are not equal.)
ndim$\mathit{ndim}$, the number of dimensions.
Constraint:
ndim ≥ 1${\mathbf{ndim}}\ge 1$.
 2:
npar – int64int32nag_int scalar
npar$\mathit{npar}$, the number of particles to be used in the swarm. Assuming all particles remain within bounds, each complete iteration will perform at least
npar function evaluations. Otherwise, significantly fewer objective function evaluations may be performed.
Default:
npar = 10 × ndim${\mathbf{npar}}=10\times {\mathbf{ndim}}$
Constraint:
npar ≥ 5 × num_threads${\mathbf{npar}}\ge 5\times \mathbf{num\_threads}$, where
num_threads is the value returned by the OpenMP environment variable
OMP_NUM_THREADS, or
num_threads is
1$1$ for a serial version of this function.
 3:
user – Any MATLAB object
user is not used by
nag_glopt_bnd_pso (e05sa), but is passed to
objfun and
monmod. Note that for large objects it may be more efficient to use a global variable which is accessible from the mfiles than to use
user.
Input Parameters Omitted from the MATLAB Interface
 iuser ruser
Output Parameters
 1:
xb(ndim) – double array
The location of the best solution found, x̃$\stackrel{~}{\mathbf{x}}$, in R^{ndim}${R}^{\mathit{ndim}}$.
 2:
fb – double scalar
The objective value of the best solution, f̃ = F(x̃)$\stackrel{~}{f}=F\left(\stackrel{~}{\mathbf{x}}\right)$.
 3:
iopts( : $:$) – int64int32nag_int array
Communication array, used to store information between calls to nag_glopt_bnd_pso (e05sa).
Note: the contents of
iopts must not have been altered between calls to
nag_glopt_optset (e05zk),
nag_glopt_optget (e05zl),
nag_glopt_bnd_pso (e05sa) and the selected problem solving routine.
 4:
opts( : $:$) – double array
Communication array, used to store information between calls to nag_glopt_bnd_pso (e05sa).
Note: the contents of
opts must not have been altered between calls to
nag_glopt_optset (e05zk),
nag_glopt_optget (e05zl),
nag_glopt_bnd_pso (e05sa) and the selected problem solving routine.
 5:
user – Any MATLAB object
 6:
itt(6$6$) – int64int32nag_int array
Integer iteration counters for
nag_glopt_bnd_pso (e05sa).
 itt(1)${\mathbf{itt}}\left(1\right)$
 Number of complete iterations.
 itt(2)${\mathbf{itt}}\left(2\right)$
 Number of complete iterations without improvement to the current optimum.
 itt(3)${\mathbf{itt}}\left(3\right)$
 Number of particles converged to the current optimum.
 itt(4)${\mathbf{itt}}\left(4\right)$
 Number of improvements to the optimum.
 itt(5)${\mathbf{itt}}\left(5\right)$
 Number of function evaluations performed.
 itt(6)${\mathbf{itt}}\left(6\right)$
 Number of particles reset.
 7:
inform – int64int32nag_int scalar
Indicates which finalization criterion was reached. The possible values of
inform are:
inform  Meaning 
< 0$<0$  Exit from a usersupplied subroutine. 
0  nag_glopt_bnd_pso (e05sa) has detected an error and terminated. 
1  The provided objective target has been achieved. (Target Objective Value). 
2  The standard deviation of the location of all the particles is below the set threshold (Swarm Standard Deviation). If the solution returned is not satisfactory, you may try setting a smaller value of Swarm Standard Deviation, or try adjusting the options governing the repulsive phase (Repulsion Initialize, Repulsion Finalize). 
3  The total number of particles converged (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 Distance Tolerance from the optimum with regard to the L^{2}${L}^{2}$ norm. If the solution is not satisfactory, you may consider lowering the Distance Tolerance. However, this may hinder the global search capability of the algorithm. 
4  The maximum number of iterations without improvement (Maximum Iterations Static) has been reached, and the required number of particles (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 Repeatability = OFF${\mathbf{Repeatability}}=\mathrm{OFF}$ may lead to an improved solution. 
5  The maximum number of iterations (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 Local Minimizer with corresponding exterior options. Otherwise if the solution is not satisfactory, you may try rerunning the application several times with Repeatability = OFF${\mathbf{Repeatability}}=\mathrm{OFF}$ and a lower iteration limit, or adjusting the options governing the repulsive phase (Repulsion Initialize, Repulsion Finalize). 
6  The maximum allowed number of function evaluations (Maximum Function Evaluations) has been reached. As with inform = 5${\mathbf{inform}}=5$, increasing this limit if the number of iterations without improvement is small, or decreasing this limit and running the algorithm multiple times with Repeatability = OFF${\mathbf{Repeatability}}=\mathrm{OFF}$, may provide a superior result. 
 8:
ifail – int64int32nag_int scalar
ifail = 0${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see
[Error Indicators and Warnings]).
For this reason, the value
− 1 or 1$1\text{ or}1$ is recommended. If the output of error messages is undesirable, then the value
1$1$ is recommended; otherwise, the recommended value is
− 1$1$.
When the value − 1 or 1$\mathbf{1}\text{ or}1$ is used it is essential to test the value of ifail on exit.nag_glopt_bnd_pso (e05sa) will return
ifail = 0${\mathbf{ifail}}={\mathbf{0}}$ if and only if a finalization criterion has been reached which can guarantee success. This may only happen if the option
Target Objective Value has been set and reached at a point within the search domain. The finalization criterion
Target Objective Value is not activated using default option settings, and must be explicitly set using
nag_glopt_optset (e05zk) if required.
nag_glopt_bnd_pso (e05sa) will return
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ 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.
Other positive values of
ifail indicate that either an error or a warning has been triggered. See
Sections [Error Indicators and Warnings],
[Accuracy] and
[Algorithmic Details] for more information.
Error Indicators and Warnings
Note: nag_glopt_bnd_pso (e05sa) may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the function:
Cases prefixed with W are classified as warnings and
do not generate an error of type NAG:error_n. See nag_issue_warnings.
 W ifail = 1${\mathbf{ifail}}=1$

A finalization criterion was reached that cannot guarantee success.
 W ifail = 2${\mathbf{ifail}}=2$

If the option
Target Warning has been activated, this indicates that the
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 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 Local Minimizer directly. 
Assuming that
objfun is correct, you may wish to set a better
Target Objective Value, or a stricter
Target Objective Tolerance.
 W ifail = 3${\mathbf{ifail}}=3$

User requested exit during call to
monmod.
User requested exit during call to
monmod.
User requested exit during call to
objfun.
User requested exit during call to
objfun.
User requested exit during call to
objfun.
User requested exit during call to
objfun.
User requested exit during call to
objfun.
User requested exit during call to
objfun.
 ifail = 11${\mathbf{ifail}}=11$

Constraint:
ndim ≥ 1${\mathbf{ndim}}\ge 1$.
 ifail = 12${\mathbf{ifail}}=12$

Constraint:
npar ≥ 5 × num_threads${\mathbf{npar}}\ge 5\times \mathbf{num\_threads}$, where
num_threads is the value returned by the OpenMP environment variable
OMP_NUM_THREADS, or
num_threads is
1$1$ for a serial version of this function.
 ifail = 14${\mathbf{ifail}}=14$

Constraint:
bu(i) ≥ bl(i)${\mathbf{bu}}\left(i\right)\ge {\mathbf{bl}}\left(i\right)$ for all
i$i$.
On entry,
bl(i) = bu(i)${\mathbf{bl}}\left(i\right)={\mathbf{bu}}\left(i\right)$ for all
i$i$.
Constraint:
bu(i) > bl(i)${\mathbf{bu}}\left(i\right)>{\mathbf{bl}}\left(i\right)$ for at least one
i$i$.
 ifail = 19${\mathbf{ifail}}=19$

Error _$\_$ occurred whilst adjusting to exterior local minimizer options.
Error _$\_$ occurred whilst adjusting to interior local minimizer options.
 ifail = 21${\mathbf{ifail}}=21$

Either the option arrays have not been initialized for nag_glopt_bnd_pso (e05sa), or they have become corrupted.
Either the option arrays have not been initialized for nag_glopt_bnd_pso (e05sa), or they have become corrupted.
Either the option arrays have not been initialized for nag_glopt_bnd_pso (e05sa), or they have become corrupted.
Either the option arrays have not been initialized for nag_glopt_bnd_pso (e05sa), or they have become corrupted.
Either the option arrays have not been initialized for nag_glopt_bnd_pso (e05sa), or they have become corrupted.
Either the option arrays have not been initialized for nag_glopt_bnd_pso (e05sa), or they have become corrupted.
 ifail = 32${\mathbf{ifail}}=32$

Derivative checks indicate possible errors in the supplied derivatives.
Gradient checks may be disabled by setting
Verify Gradients = OFF${\mathbf{Verify\; Gradients}}=\mathrm{OFF}$.
Derivative checks indicate possible errors in the supplied derivatives.
Gradient checks may be disabled by setting
Verify Gradients = OFF${\mathbf{Verify\; Gradients}}=\mathrm{OFF}$.
Derivative checks indicate possible errors in the supplied derivatives.
Gradient checks may be disabled by setting
Verify Gradients = OFF${\mathbf{Verify\; Gradients}}=\mathrm{OFF}$.
 ifail = − 999${\mathbf{ifail}}=999$

Dynamic memory allocation failed.
Accuracy
If
ifail = 0${\mathbf{ifail}}={\mathbf{0}}$ (or
ifail = 2${\mathbf{ifail}}={\mathbf{2}}$) or
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ on exit, either a
Target Objective Value or finalization criterion has 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 [Algorithmic Details] and
[Optional Parameters]).
Provided the objective function and constraints are sufficiently well behaved, if a local minimizer is used in conjunction with nag_glopt_bnd_pso (e05sa), 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 basin of attraction of superior local optima. Using the options
Repulsion Initialize and
Repulsion Finalize described in
Section [Optional Parameters] 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,
ifail = 0${\mathbf{ifail}}={\mathbf{0}}$. This may only happen if a
Target Objective Value is assigned and is reached by the algorithm.
On successful exit without guaranteed success,
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ 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.
Further Comments
The memory used by
nag_glopt_bnd_pso (e05sa) is relatively static throughout. As such,
nag_glopt_bnd_pso (e05sa) may be used in problems with high dimension number (
ndim > 100${\mathbf{ndim}}>100$) 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 parameters and options are chosen to accelerate convergence at the expense of the global search. However, the option
Repeatability = ON${\mathbf{Repeatability}}=\mathrm{ON}$ may be set to initialize the internal random number generator using a preset seed, which will result in identical solutions being obtained.
Example
Open in the MATLAB editor:
nag_glopt_bnd_pso_example
function nag_glopt_bnd_pso_example
npar = int64(5);
bu = [ 500, 500];
bl = [500, 500];
x_target = [420.9687463599820;420.9687463599820];
f_target = 837.9657745448674;
iopts = zeros(100, 1, 'int64');
opts = zeros(100, 1);
[iopts, opts, ifail] = nag_glopt_optset('Initialize = nag_glopt_bnd_pso', iopts, opts);
fprintf('\nDefault Option Queries:\n\n');
[ivalue, rvalue, cvalue, optype, ifail] = ...
nag_glopt_optget('Boundary', iopts, opts);
fprintf('Boundary : %s\n', cvalue);
[ivalue, rvalue, cvalue, optype, ifail] = ...
nag_glopt_optget('Maximum Iterations Completed', iopts, opts);
fprintf('Maximum Iterations Completed : %d (%s)\n', ivalue, strtrim(cvalue));
[ivalue, rvalue, cvalue, optype, ifail] = ...
nag_glopt_optget('Distance Tolerance', iopts, opts);
fprintf('Distance Tolerance : %15.4e\n', rvalue);
fprintf('\n1. Solution without using coupled local minimizer.\n');
[iopts, opts, ifail] = nag_glopt_optset('Repeatability = On', iopts, opts);
[iopts, opts, ifail] = nag_glopt_optset('Verify Gradients = Off', iopts, opts);
[iopts, opts, ifail] = nag_glopt_optset('Boundary = Hyperspherical', iopts, opts);
[iopts, opts, ifail] = nag_glopt_optset('Maximum iterations static = 150', iopts, opts);
[iopts, opts, ifail] = nag_glopt_optset('Repulsion Initialize = 30', iopts, opts);
[iopts, opts, ifail] = nag_glopt_optset('Repulsion Finalize = 30', iopts, opts);
[xb, fb, iopts, opts, user, itt, inform, ifail] = ...
nag_glopt_bnd_pso(bl, bu, @objfun, 'nag_glopt_bnd_pso_dummy_monmod', ...
iopts, opts, 'npar', npar);
switch ifail
case {0,1}
display_result(x_target, f_target, xb,fb,itt,inform);
case 3
display_result(x_target, f_target, xb,fb,itt,inform);
otherwise
end
fprintf('\n2. Solution using coupled local minimizer nag_opt_uncon_simplex.\n');
optstr = sprintf('Target Objective Value = %32.16e', f_target);
[iopts, opts, ifail] = nag_glopt_optset(optstr, iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Target Objective Tolerance = 1.0e5', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Target Objective Safeguard = 1.0e8', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Minimizer = nag_opt_uncon_simplex', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Interior Iterations = 10', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Exterior Iterations = 20', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Interior Tolerance = 1.0e4', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Exterior Tolerance = 1.0e4', iopts, opts);
[xb, fb, iopts, opts, user, itt, inform, ifail] = ...
nag_glopt_bnd_pso(bl, bu, @objfun, 'nag_glopt_bnd_pso_dummy_monmod', ...
iopts, opts, 'npar', npar);
switch ifail
case {0,1}
display_result(x_target, f_target, xb,fb,itt,inform);
case 3
display_result(x_target, f_target, xb,fb,itt,inform);
otherwise
end
fprintf('\n3. Solution using coupled local minimizer nag_opt_uncon_conjgrd_comp.\n');
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Minimizer = nag_opt_uncon_conjgrd_comp', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Interior Iterations = 5', iopts, opts);
[iopts, opts, ifail] = ...
nag_glopt_optset('Local Exterior Iterations = 20', iopts, opts);
[xb, fb, iopts, opts, user, itt, inform, ifail] = ...
nag_glopt_bnd_pso(bl, bu, @objfun, 'nag_glopt_bnd_pso_dummy_monmod', ...
iopts, opts, 'npar', npar);
switch ifail
case {0,1}
display_result(x_target, f_target, xb,fb,itt,inform);
case 3
display_result(x_target, f_target, xb,fb,itt,inform);
otherwise
end
function [mode, objf, vecout, user] = objfun(mode, ndim, x, objf, vecout, nstate, user)
switch nstate
case (2)
case (1)
case (1)
case (0)
otherwise
mode = int64(1);
error('*** Error detected in objfun');
end
evalobjf = false;
evalobjg = false;
switch mode
case {0,5}
evalobjf = true;
case {1,6}
evalobjg = true;
case {2,7}
evalobjf = true;
evalobjg = true;
otherwise
mode = int64(1);
error('*** Illegal value of mode (%d) in objfun', mode);
end
if evalobjf
objf = sum(x(1:double(ndim)).*sin(sqrt(abs(x(1:double(ndim))))));
end
if evalobjg
vecout = sqrt(abs(x));
for i=1:double(ndim)
if abs(x(i)) < nag_machine_precision
vecout(i) = 0;
else
vecout(i) = sin(vecout(i)) + ...
signtransfer(x(i)*cos(vecout(i))/(2.0*vecout(i)), x(i));
end
end
end
function [r] = signtransfer(x, y)
if y >= 0
r = abs(x);
else
r = abs(x);
end
function [] = display_result(x_target, f_target, xb,fb,itt,inform)
fprintf('\nAlgorithm Statistics\n\n');
fprintf('Total complete iterations : %d\n', itt(1));
fprintf('Complete iterations since improvement : %d\n', itt(2));
fprintf('Total particles converged to xb : %d\n', itt(3));
fprintf('Total improvements to global optimum : %d\n', itt(4));
fprintf('Total function evaluations : %d\n', itt(5));
fprintf('Total particles reinitialized : %d\n\n', itt(6));
switch inform
case 0
fprintf('Solution Status : An error was detected by nag_glopt_bnd_pso\n');
case 1
fprintf('Solution Status : Target value achieved\n');
case 2
fprintf('Solution Status : Minimum swarm standard deviation obtained\n');
case 3
fprintf('Solution Status : Sufficient particles converged\n');
case 4
fprintf('Solution Status : No improvement in preset iteration limit\n');
case 5
fprintf('Solution Status : Maximum complete iterations attained\n');
case 6
fprintf('Solution Status : Maximum function evaluations exceeded\n');
otherwise
fprintf('User termination case: %d\n', inform);
end
fprintf('\n Known objective optimum : %13.5f\n', f_target);
fprintf(' Achieved objective value : %13.5f\n\n', fb);
title = 'Comparison between known and achieved optima.';
clabs = {'x_target'; 'xb '};
[ifail] = ...
nag_file_print_matrix_real_gen_comp('G', 'N', [x_target, xb], 'f9.2', title, 'I', clabs, ...
'C', clabs, int64(80), int64(0));
fprintf('\n');
Default Option Queries:
Boundary : FLOATING
Maximum Iterations Completed : 1000 (DEFAULT)
Distance Tolerance : 1.0000e04
1. Solution without using coupled local minimizer.
Warning: nag_glopt_bnd_pso (e05sa) returned a warning indicator (1)
Algorithm Statistics

Total complete iterations : 395
Complete iterations since improvement : 152
Total particles converged to xb : 2
Total improvements to global optimum : 59
Total function evaluations : 2773
Total particles reinitialized : 2
Solution Status : No improvement in preset iteration limit
Known objective optimum : 837.96577
Achieved objective value : 837.96567
Comparison between known and achieved optima.
x_target xb
1 420.97 420.95
2 420.97 420.94
2. Solution using coupled local minimizer nag_opt_uncon_simplex.
Algorithm Statistics

Total complete iterations : 51
Complete iterations since improvement : 1
Total particles converged to xb : 0
Total improvements to global optimum : 12
Total function evaluations : 537
Total particles reinitialized : 0
Solution Status : Target value achieved
Known objective optimum : 837.96577
Achieved objective value : 837.96577
Comparison between known and achieved optima.
x_target xb
1 420.97 420.97
2 420.97 420.97
3. Solution using coupled local minimizer nag_opt_uncon_conjgrd_comp.
Algorithm Statistics

Total complete iterations : 8
Complete iterations since improvement : 1
Total particles converged to xb : 0
Total improvements to global optimum : 10
Total function evaluations : 120
Total particles reinitialized : 0
Solution Status : Target value achieved
Known objective optimum : 837.96577
Achieved objective value : 837.96561
Comparison between known and achieved optima.
x_target xb
1 420.97 420.94
2 420.97 420.98
Open in the MATLAB editor:
e05sa_example
function e05sa_example
bu = [ 500, 500];
bl = [500, 500];
npar = int64(5);
x_target = [420.9687463599820;420.9687463599820];
f_target = 837.9657745448674;
iopts = zeros(100, 1, 'int64');
opts = zeros(100, 1);
[iopts, opts, ifail] = e05zk('Initialize = e05sa', iopts, opts);
fprintf('\nDefault Option Queries:\n\n');
[ivalue, rvalue, cvalue, optype, ifail] = ...
e05zl('Boundary', iopts, opts);
fprintf('Boundary : %s\n', cvalue);
[ivalue, rvalue, cvalue, optype, ifail] = ...
e05zl('Maximum Iterations Completed', iopts, opts);
fprintf('Maximum Iterations Completed : %d (%s)\n', ivalue, strtrim(cvalue));
[ivalue, rvalue, cvalue, optype, ifail] = ...
e05zl('Distance Tolerance', iopts, opts);
fprintf('Distance Tolerance : %15.4e\n', rvalue);
fprintf('\n1. Solution without using coupled local minimizer.\n');
[iopts, opts, ifail] = e05zk('Repeatability = On', iopts, opts);
[iopts, opts, ifail] = e05zk('Verify Gradients = Off', iopts, opts);
[iopts, opts, ifail] = e05zk('Boundary = Hyperspherical', iopts, opts);
[iopts, opts, ifail] = e05zk('Maximum iterations static = 150', iopts, opts);
[iopts, opts, ifail] = e05zk('Repulsion Initialize = 30', iopts, opts);
[iopts, opts, ifail] = e05zk('Repulsion Finalize = 30', iopts, opts);
[xb, fb, iopts, opts, user, itt, inform, ifail] = ...
e05sa(bl, bu, @objfun, 'e05sxm', iopts, opts, 'npar', npar);
switch ifail
case {0,1}
display_result(x_target, f_target, xb,fb,itt,inform);
case 3
display_result(x_target, f_target, xb,fb,itt,inform);
otherwise
end
fprintf('\n2. Solution using coupled local minimizer e04cb.\n');
optstr = sprintf('Target Objective Value = %32.16e', f_target);
[iopts, opts, ifail] = e05zk(optstr, iopts, opts);
[iopts, opts, ifail] = e05zk('Target Objective Tolerance = 1.0e5', iopts, opts);
[iopts, opts, ifail] = e05zk('Target Objective Safeguard = 1.0e8', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Minimizer = e04cb', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Interior Iterations = 10', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Exterior Iterations = 20', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Interior Tolerance = 1.0e4', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Exterior Tolerance = 1.0e4', iopts, opts);
[xb, fb, iopts, opts, user, itt, inform, ifail] = ...
e05sa(bl, bu, @objfun, 'e05sxm', iopts, opts, 'npar', npar);
switch ifail
case {0,1}
display_result(x_target, f_target, xb,fb,itt,inform);
case 3
display_result(x_target, f_target, xb,fb,itt,inform);
otherwise
end
fprintf('\n3. Solution using coupled local minimizer e04dg.\n');
[iopts, opts, ifail] = e05zk('Local Minimizer = e04dg', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Interior Iterations = 5', iopts, opts);
[iopts, opts, ifail] = e05zk('Local Exterior Iterations = 20', iopts, opts);
[xb, fb, iopts, opts, user, itt, inform, ifail] = ...
e05sa(bl, bu, @objfun, 'e05sxm', iopts, opts, 'npar', npar);
switch ifail
case {0,1}
display_result(x_target, f_target, xb,fb,itt,inform);
case 3
display_result(x_target, f_target, xb,fb,itt,inform);
otherwise
end
function [mode, objf, vecout, user] = objfun(mode, ndim, x, objf, vecout, nstate, user)
switch nstate
case (2)
case (1)
case (1)
case (0)
otherwise
mode = int64(1);
error('*** Error detected in objfun');
end
evalobjf = false;
evalobjg = false;
switch mode
case {0,5}
evalobjf = true;
case {1,6}
evalobjg = true;
case {2,7}
evalobjf = true;
evalobjg = true;
otherwise
mode = int64(1);
error('*** Illegal value of mode (%d) in objfun', mode);
end
if evalobjf
objf = sum(x(1:double(ndim)).*sin(sqrt(abs(x(1:double(ndim))))));
end
if evalobjg
vecout = sqrt(abs(x));
for i=1:double(ndim)
if abs(x(i)) < x02aj
vecout(i) = 0;
else
vecout(i) = sin(vecout(i)) + ...
signtransfer(x(i)*cos(vecout(i))/(2.0*vecout(i)), x(i));
end
end
end
function [r] = signtransfer(x, y)
if y >= 0
r = abs(x);
else
r = abs(x);
end
function [] = display_result(x_target, f_target, xb,fb,itt,inform)
fprintf('\nAlgorithm Statistics\n\n');
fprintf('Total complete iterations : %d\n', itt(1));
fprintf('Complete iterations since improvement : %d\n', itt(2));
fprintf('Total particles converged to xb : %d\n', itt(3));
fprintf('Total improvements to global optimum : %d\n', itt(4));
fprintf('Total function evaluations : %d\n', itt(5));
fprintf('Total particles reinitialized : %d\n\n', itt(6));
switch inform
case 0
fprintf('Solution Status : An error was detected by e05sa\n');
case 1
fprintf('Solution Status : Target value achieved\n');
case 2
fprintf('Solution Status : Minimum swarm standard deviation obtained\n');
case 3
fprintf('Solution Status : Sufficient particles converged\n');
case 4
fprintf('Solution Status : No improvement in preset iteration limit\n');
case 5
fprintf('Solution Status : Maximum complete iterations attained\n');
case 6
fprintf('Solution Status : Maximum function evaluations exceeded\n');
otherwise
fprintf('User termination case: %d\n', inform);
end
fprintf('\n Known objective optimum : %13.5f\n', f_target);
fprintf(' Achieved objective value : %13.5f\n\n', fb);
title = 'Comparison between known and achieved optima.';
clabs = {'x_target'; 'xb '};
[ifail] = x04cb('G', 'N', [x_target, xb], 'f9.2', title, 'I', clabs, ...
'C', clabs, int64(80), int64(0));
fprintf('\n');
Default Option Queries:
Boundary : FLOATING
Maximum Iterations Completed : 1000 (DEFAULT)
Distance Tolerance : 1.0000e04
1. Solution without using coupled local minimizer.
Warning: nag_glopt_bnd_pso (e05sa) returned a warning indicator (1)
Algorithm Statistics

Total complete iterations : 395
Complete iterations since improvement : 152
Total particles converged to xb : 2
Total improvements to global optimum : 59
Total function evaluations : 2773
Total particles reinitialized : 2
Solution Status : No improvement in preset iteration limit
Known objective optimum : 837.96577
Achieved objective value : 837.96567
Comparison between known and achieved optima.
x_target xb
1 420.97 420.95
2 420.97 420.94
2. Solution using coupled local minimizer e04cb.
Algorithm Statistics

Total complete iterations : 51
Complete iterations since improvement : 1
Total particles converged to xb : 0
Total improvements to global optimum : 12
Total function evaluations : 537
Total particles reinitialized : 0
Solution Status : Target value achieved
Known objective optimum : 837.96577
Achieved objective value : 837.96577
Comparison between known and achieved optima.
x_target xb
1 420.97 420.97
2 420.97 420.97
3. Solution using coupled local minimizer e04dg.
Algorithm Statistics

Total complete iterations : 8
Complete iterations since improvement : 1
Total particles converged to xb : 0
Total improvements to global optimum : 10
Total function evaluations : 120
Total particles reinitialized : 0
Solution Status : Target value achieved
Known objective optimum : 837.96577
Achieved objective value : 837.96561
Comparison between known and achieved optima.
x_target xb
1 420.97 420.94
2 420.97 420.98
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.
n_{p}${n}_{p}$ 
the number of particles, npar 
ℓ_{box}${\mathbf{\ell}}_{\mathrm{box}}$ 
array of ndim lower box bounds 
u_{box}${\mathbf{u}}_{\mathrm{box}}$ 
array of ndim upper box bounds 
x_{j}${\mathbf{x}}_{j}$ 
position of particle j$j$ 
x̂_{j}${\hat{\mathbf{x}}}_{j}$ 
best position found by particle j$j$ 
x̃$\stackrel{~}{\mathbf{x}}$ 
best position found by any particle 
f_{j}${f}_{j}$ 
F(x_{j})$F\left({\mathbf{x}}_{j}\right)$ 
f̂_{j}${\hat{f}}_{j}$ 
F(x̂_{j})$F\left({\hat{\mathbf{x}}}_{j}\right)$, best value found by particle j$j$ 
f̃$\stackrel{~}{f}$ 
F(x̃)$F\left(\stackrel{~}{\mathbf{x}}\right)$, best value found by any particle 
v_{j}${\mathbf{v}}_{j}$ 
velocity of particle j$j$ 
w_{j}${w}_{j}$ 
weight on v_{j}${\mathbf{v}}_{j}$ for velocity update, decreasing according to Weight Decrease 
V_{max}${V}_{\mathrm{max}}$ 
maximum absolute velocity, dependent upon Maximum Variable Velocity 
I_{c}${I}_{c}$ 
swarm iteration counter 
I_{s}${I}_{s}$ 
iterations since x̃$\stackrel{~}{\mathbf{x}}$ was updated 
D_{1}${\mathbf{D}}_{1}$,D_{2}${\mathbf{D}}_{2}$ 
diagonal matrices with random elements in range (0,1)$(0,1)$ 
C_{s}${C}_{s}$ 
the cognitive advance coefficient which weights velocity towards x̂_{j}${\hat{\mathbf{x}}}_{j}$, adjusted using Advance Cognitive 
C_{g}${C}_{g}$ 
the global advance coefficient which weights velocity towards x̃$\stackrel{~}{\mathbf{x}}$, adjusted using Advance Global 
dtol$\mathit{dtol}$ 
the Distance Tolerance for resetting a converged particle 
R ∈ U(ℓ_{box},u_{box})$\mathbf{R}\in U({\mathbf{\ell}}_{\mathrm{box}},{\mathbf{u}}_{\mathrm{box}})$ 
an array of random numbers whose i$i$th element is drawn from a uniform distribution in the range (ℓ_{box}_{i},u_{box}_{i})$({{\mathbf{\ell}}_{\mathrm{box}}}_{\mathit{i}},{{\mathbf{u}}_{\mathrm{box}}}_{\mathit{i}})$, for i = 1,2, … ,ndim$\mathit{i}=1,2,\dots ,{\mathbf{ndim}}$ 
O_{i}${O}_{i}$ 
local optimizer interior options 
O_{e}${O}_{e}$ 
local optimizer exterior options 
LOCMIN(x,f,O)$\mathrm{LOCMIN}(\mathbf{x},f,O)$ 
apply local optimizer using the set of options O$O$ using the solution (x,f)$(\mathbf{x},f)$ as the starting point, if used (not default) 
monmod 
monitor progress and possibly modify x_{j}${\mathbf{x}}_{j}$ 
BOUNDARY 
apply required behaviour for x_{j}${\mathbf{x}}_{j}$ outside bounding box, (see Boundary) 
new (f̃$\stackrel{~}{f}$) 
true if x̃$\stackrel{~}{\mathbf{x}}$, c̃$\stackrel{~}{\mathbf{c}}$, f̃$\stackrel{~}{f}$ were updated at this iteration 
Additionally a repulsion phase can be introduced by changing from the default values of options
Repulsion Finalize (
r_{f}${r}_{f}$),
Repulsion Initialize (
r_{i}${r}_{i}$) and
Repulsion Particles (
r_{p}${r}_{p}$). If the number of static particles is denoted
n_{s}${n}_{s}$ then the following can be inserted after the new(
f̃$\stackrel{~}{f}$) check in the pseudocode above.
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 [Description of the optional parameters].
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$a$, i and r$i\text{ and}r$ denote options that take character, integer and real values respectively;
 the default value, where the symbol ε$\epsilon $ is a generic notation for machine precision (see nag_machine_precision (x02aj)), and Imax$\mathit{Imax}$ represents the largest representable integer value (see nag_machine_integer_max (x02bb)).
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
nag_glopt_bnd_pso (e05sa) the maximum length of the parameter
cvalue used by
nag_glopt_optget (e05zl) is
15$15$.
Advance Cognitive r$r$Default = 2.0$\text{}=2.0$The cognitive advance coefficient, C_{s}${C}_{s}$. When larger than the global advance coefficient, this will cause particles to be attracted toward their previous best positions. Setting r = 0.0$r=0.0$ will cause nag_glopt_bnd_pso (e05sa) to act predominantly as a local optimizer. Setting r > 2.0$r>2.0$ 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$r$Default = 2.0$\text{}=2.0$The global advance coefficient, C_{g}${C}_{g}$. When larger than the cognitive coefficient this will encourage convergence toward the best solution yet found. Values r ∈ (0,1)$r\in (0,1)$ will inhibit particles overshooting the optimum. Values r ∈ [1,2)$r\in [1,2)$ cause particles to fly over the optimum some of the time. Larger values can prohibit convergence. Setting r = 0.0$r=0.0$ 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$a$Default = FLOATING$\text{}=\text{FLOATING}$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
bl(i) ≠ bu(i)${\mathbf{bl}}\left(i\right)\ne {\mathbf{bu}}\left(i\right)$,
i = 1,2, … ,ndim$i=1,2,\dots ,{\mathbf{ndim}}$.
 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. x̂_{j}${\hat{\mathbf{x}}}_{j}$ and f̂_{j}${\hat{f}}_{j}$ 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 ndim$\mathit{ndim}$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$0.0$.
Distance Scaling a$a$Default = ON$\text{}=\mathrm{ON}$Determines whether distances should be scaled by box widths.
 ON
 When a distance is calculated between x$\mathbf{x}$ and y$\mathbf{y}$, a scaled L^{2}${L}^{2}$ norm is used.
 OFF
 Distances are calculated as the standard L^{2}${L}^{2}$ norm without any rescaling.
Distance Tolerance r$r$Default = 10^{4}$\text{}={10}^{4}$This is the distance, dtol$\mathit{dtol}$ 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$r>0.0$.
Function Precision r$r$Default = ε^{0.9}$\text{}={\epsilon}^{0.9}$The parameter defines ε_{r}${\epsilon}_{r}$, which is intended to be a measure of the accuracy with which the problem function F(x)$F\left(\mathbf{x}\right)$ can be computed. If r < ε$r<\epsilon $ or r ≥ 1$r\ge 1$, the default value is used.
The value of
ε_{r}${\epsilon}_{r}$ should reflect the relative precision of
1 + F(x)$1+\leftF\left(\mathbf{x}\right)\right$; i.e.,
ε_{r}${\epsilon}_{r}$ acts as a relative precision when
F$\leftF\right$ is large, and as an absolute precision when
F$\leftF\right$ is small. For example, if
F(x)$F\left(\mathbf{x}\right)$ is typically of order
1000$1000$ and the first six significant digits are known to be correct, an appropriate value for
ε_{r}${\epsilon}_{r}$ would be
10^{ − 6}${10}^{6}$. In contrast, if
F(x)$F\left(\mathbf{x}\right)$ is typically of order
10^{ − 4}${10}^{4}$ and the first six significant digits are known to be correct, an appropriate value for
ε_{r}${\epsilon}_{r}$ would be
10^{ − 10}${10}^{10}$. The choice of
ε_{r}${\epsilon}_{r}$ 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
ε_{r}${\epsilon}_{r}$ 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$r$Default = 0.5$\text{}=0.5$Contracts the box boundaries used by a box constrained local minimizer to,
[β_{l},β_{u}]$[{\beta}_{l},{\beta}_{u}]$, containing the start point
x$x$, where
Smaller values of
r$r$ 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 ≤ r ≤ 1.0$0.0\le r\le 1.0$.
Local Interior Iterations i_{1}${i}_{1}$Local Interior Major Iterations i_{1}${i}_{1}$Local Exterior Iterations i_{2}${i}_{2}$Local Exterior Major Iterations i_{2}${i}_{2}$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:
Unless set, these are functions of the parameters passed to nag_glopt_bnd_pso (e05sa).
Setting
i = 0$i=0$ will disable the local minimizer in the corresponding algorithmic region. For example, setting
Local Interior Iterations = 0${\mathbf{Local\; Interior\; Iterations}}=0$ and
Local Exterior Iterations = 30${\mathbf{Local\; Exterior\; Iterations}}=30$ will cause the algorithm to perform no local minimizations inside the main loop of the algorithm, and a local minimization with upto
30$30$ iterations after the main loop has been exited.
Note: currently
nag_opt_bounds_quasi_func_easy (e04jy) or
nag_opt_bounds_mod_deriv_easy (e04kz) are restricted to using
400 × ndim$400\times {\mathbf{ndim}}$ and
50 × ndim$50\times {\mathbf{ndim}}$ as function evaluation limits respectively. This applies to both local minimizations inside and outside the main loop. They may still be deactivated in either phase by setting
i = 0$i=0$, and may subsequently be reactivated in either phase by setting
i > 0$i>0$.
Constraint:
i_{1} ≥ 0${i}_{1}\ge 0$, i_{2} ≥ 0${i}_{2}\ge 0$.
Local Interior Tolerance r_{1}${r}_{1}$Default = 10^{4}$\text{}={10}^{4}$Local Exterior Tolerance r_{2}${r}_{2}$Default = 10^{4}$\text{}={10}^{4}$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${r}_{1}>0.0$, r_{2} > 0.0${r}_{2}>0.0$.
Local Interior Minor Iterations i_{1}${i}_{1}$Local Exterior Minor Iterations i_{2}${i}_{2}$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 
nag_opt_nlp1_solve (e04uc) 
Minor Iteration Limit 
max (10,2 × ndim)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}(10,2\times {\mathbf{ndim}})$ 
max (30,3 × ndim)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}(30,3\times {\mathbf{ndim}})$ 
Constraint:
i_{1} ≥ 0${i}_{1}\ge 0$, i_{2} ≥ 0${i}_{2}\ge 0$.
Local Minimizer a$a$Default = OFF$\text{}=\mathrm{OFF}$Allows for a choice of
Chapter E04 functions to be used as a coupled, dedicated local minimizer.
 OFF
 No local minimization will be performed in either the INTERIOR or EXTERIOR sections of the algorithm.
 nag_opt_uncon_simplex (e04cb)
 Use nag_opt_uncon_simplex (e04cb) as the local minimizer. This does not require the calculation of derivatives.
On a call to
objfun during a local minimization,
mode = 5${\mathbf{mode}}=5$.
 nag_opt_bounds_mod_deriv_easy (e04kz)
 Use nag_opt_bounds_mod_deriv_easy (e04kz) as the local minimizer. This requires the calculation of derivatives in objfun, as indicated by mode.
The box bounds forwarded to this function from
nag_glopt_bnd_pso (e05sa) will have been acted upon by
Local Boundary Restriction. As such, the domain exposed may be greatly smaller than that provided to
nag_glopt_bnd_pso (e05sa).
Accurate derivatives must be provided to this function, and will not be approximated internally. Each iteration of this local minimizer also requires the calculation of both the objective function and its derivative. Hence on a call to
objfun during a local minimization,
mode = 7${\mathbf{mode}}=7$.
 nag_opt_bounds_quasi_func_easy (e04jy)
 Use nag_opt_bounds_quasi_func_easy (e04jy) as the local minimizer. This does not require the calculation of derivatives.
On a call to
objfun during a local minimization,
mode = 5${\mathbf{mode}}=5$.
The box bounds forwarded to this function from
nag_glopt_bnd_pso (e05sa) will have been acted upon by
Local Boundary Restriction. As such, the domain exposed may be greatly smaller than that provided to
nag_glopt_bnd_pso (e05sa).
 nag_opt_uncon_conjgrd_comp (e04dg)
(e04dg)
 Use (e04dg) 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,
mode = 5${\mathbf{mode}}=5$ or
7$7$.
 nag_opt_nlp1_solve (e04uc)
(e04uc)
 Use (e04uc) as the local minimizer.
This operates such that any derivatives of the objective function that 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,
mode = 1${\mathbf{mode}}=1$,
2$2$ or
5$5$.
The box bounds forwarded to this function from
nag_glopt_bnd_pso (e05sa) will have been acted upon by
Local Boundary Restriction. As such, the domain exposed may be greatly smaller than that provided to
nag_glopt_bnd_pso (e05sa).
Maximum Function Evaluations i$i$Default = Imax$=\mathit{Imax}$The maximum number of evaluations of the objective function. When reached this will return
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ and
inform = 6${\mathbf{inform}}=6$.
Constraint:
i > 0$i>0$.
Maximum Iterations Completed i$i$Default = 1000 × ndim$\text{}=1000\times {\mathbf{ndim}}$The maximum number of complete iterations that may be performed. Once exceeded
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ and
inform = 5${\mathbf{inform}}=5$.
Unless set, this adapts to the parameters passed to nag_glopt_bnd_pso (e05sa).
Constraint:
i ≥ 1$i\ge 1$.
Maximum Iterations Static i$i$Default = 100$\text{}=100$The maximum number of iterations without any improvement to the current global optimum. If exceeded
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ and
inform = 4${\mathbf{inform}}=4$. This exit will be hindered by setting
Maximum Iterations Static Particles to larger values.
Constraint:
i ≥ 1$i\ge 1$.
Maximum Iterations Static Particles i$i$Default = 0$\text{}=0$The minimum number of particles that must have converged to the current optimum before the function may exit due to
Maximum Iterations Static with
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ and
inform = 4${\mathbf{inform}}=4$.
Constraint:
i ≥ 0$i\ge 0$.
Maximum Particles Converged i$i$Default = Imax$=\mathit{Imax}$The maximum number of particles that may converge to the current optimum. When achieved,
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ and
inform = 3${\mathbf{inform}}=3$. This exit will be hindered by setting ‘
Repulsion’ options, as these cause the swarm to reexpand.
Constraint:
i > 0$i>0$.
Maximum Particles Reset i$i$Default = Imax$=\mathit{Imax}$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
Distance Tolerance of the global optimum will continue to evolve as normal.
Constraint:
i > 0$i>0$.
Maximum Variable Velocity r$r$Default = 0.25$\text{}=0.25$Along any dimension j$j$, the absolute velocity is bounded above by v_{j} ≤ r × (u_{j} − ℓ_{j}) = V_{max}$\left{\mathbf{v}}_{j}\right\le r\times ({\mathbf{u}}_{j}{\mathbf{\ell}}_{j})={\mathbf{V}}_{\mathrm{max}}$. 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$4.0$ may cause significant and potentially unrecoverable swarm divergence.
Constraint:
r > 0.0$r>0.0$.
Optimize a$a$Default = MINIMIZE$\text{}=\mathrm{MINIMIZE}$Determines whether to maximize or minimize the objective function.
 MINIMIZE
 The objective function will be minimized.
 MAXIMIZE
 The objective function will be maximized. This is accomplished by minimizing the negative of the objective.
Repeatability a$a$Default = OFF$\text{}=\mathrm{OFF}$Allows for the same random number generator seed to be used for every call to
nag_glopt_bnd_pso (e05sa).
Repeatability = OFF${\mathbf{Repeatability}}=\mathrm{OFF}$ is recommended in general.
 OFF
 The internal generation of random numbers will be nonrepeatable.
 ON
 The same seed will be used.
Repulsion Finalize i$i$Default = Imax$=\mathit{Imax}$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 ≥ 2$i\ge 2$.
Repulsion Initialize i$i$Default = Imax$=\mathit{Imax}$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 ≥ 2$i\ge 2$.
Repulsion Particles i$i$Default = 0$\text{}=0$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 ≥ 0$i\ge 0$.
Swarm Standard Deviation r$r$Default = 0.1$\text{}=0.1$The target standard deviation of the particle distances from the current optimum. Once the standard deviation is below this level,
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 1${\mathbf{ifail}}={\mathbf{1}}$ and
inform = 2${\mathbf{inform}}=2$. 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.
In SMP parallel implementations of
nag_glopt_bnd_pso (e05sa), the standard deviation will be calculated based only on the particles local to the particular thread that checks for finalization. Considerably fewer particles may be used in this calculation than when the algorithm is run in serial. It is therefore recommended that you provide a smaller value of
Swarm Standard Deviation when running in parallel than when running in serial.
Constraint:
r ≥ 0.0$r\ge 0.0$.
Target Objective a$a$Default = OFF$\text{}=\mathrm{OFF}$Target Objective Value r$r$Default = 0.0$\text{}=0.0$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
Target Warning is active)
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 0${\mathbf{ifail}}={\mathbf{0}}$ and
inform = 1${\mathbf{inform}}=1$. Other than checking for feasibility only (
Optimize = CONSTRAINTS${\mathbf{Optimize}}=\mathrm{CONSTRAINTS}$), 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
Target Warning is active,
nag_glopt_bnd_pso (e05sa) will exit with
ifail = 2${\mathbf{ifail}}={\mathbf{2}}$. This option may take any real value
r$r$, or the character ON/OFF as well as DEFAULT. If this option is queried using
nag_glopt_optget (e05zl), the current value of
r$r$ 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$r$
 Once a point is found with an objective value within the Target Objective Tolerance of r$r$, nag_glopt_bnd_pso (e05sa) will exit successfully with ifail = 0${\mathbf{ifail}}={\mathbf{0}}$ and inform = 1${\mathbf{inform}}=1$.
 OFF
 The current value of r$r$ will remain stored, however it will not be used as a finalization criterion.
 ON
 The current value of r$r$ stored will be used as a finalization criterion.
 DEFAULT
 The stored value of r$r$ will be reset to its default value (0.0$0.0$), and this finalization criterion will be deactivated.
Target Objective Safeguard r$r$Default = 100.0ε$\text{}=100.0\epsilon $If you have given a target objective value to reach in
objval$\mathit{objval}$ (the value of the optional parameter
Target Objective Value),
objsfg$\mathit{objsfg}$ sets your desired safeguarded termination tolerance, for when
objval$\mathit{objval}$ is close to zero.
Constraint:
objsfg ≥ 2ε$\mathit{objsfg}\ge 2\epsilon $.
Target Objective Tolerance r$r$Default = 0.0$\text{}=0.0$The optional tolerance to a userspecified target value.
Constraint:
r ≥ 0.0$r\ge 0.0$.
Target Warning a$a$Default = OFF$\text{}=\mathrm{OFF}$Activates or deactivates the error exit associated with the target value being achieved before entry into the main loop of the algorithm,
ifail = 2${\mathbf{ifail}}={\mathbf{2}}$.
 OFF
 No error will be returned, and the function will exit normally.
 ON
 An error will be returned if the target objective is reached prematurely, and the function will exit with ifail = 2${\mathbf{ifail}}={\mathbf{2}}$.
Verify Gradients a$a$Default = ON$\text{}=\mathrm{ON}$Adjusts the level of gradient checking performed when gradients are required. Gradient checks are only performed on the first call to the chosen local minimizer if it requires gradients. There is no guarantee that the gradient check will be correct, as the finite differences used in the gradient check are themselves subject to inaccuracies.
 OFF
 No gradient checking will be performed.
 ON
 A cheap gradient check will be performed on both the gradients corresponding to the objective through objfun.
 OBJECTIVE
FULL
 A more expensive gradient check will be performed on the gradients corresponding to the objective objfun.
Weight Decrease a$a$Default = INTEREST$\text{}=\mathrm{INTEREST}$Determines how particle weights decrease.
 OFF
 Weights do not decrease.
 INTEREST
 Weights decrease through compound interest as w_{IT + 1} = w_{IT}(1 − W_{val})${w}_{\mathit{IT}+1}={w}_{\mathit{IT}}(1{W}_{\mathit{val}})$, where W_{val}${W}_{\mathit{val}}$ is the Weight Value and IT$\mathit{IT}$ is the current number of iterations.
 LINEAR
 Weights decrease linearly following w_{IT + 1} = w_{IT} − IT × (W_{max} − W_{min}) / IT_{max}${w}_{\mathit{IT}+1}={w}_{\mathit{IT}}\mathit{IT}\times ({W}_{\mathit{max}}{W}_{\mathit{min}})/{\mathit{IT}}_{\mathit{max}}$, where IT$\mathit{IT}$ is the iteration number and IT_{max}${\mathit{IT}}_{\mathit{max}}$ is the maximum number of iterations as set by Maximum Iterations Completed.
Weight Initial r$r$Default = W_{max}$\text{}={W}_{\mathit{max}}$The initial value of any particle's inertial weight,
W_{ini}${W}_{\mathit{ini}}$, or the minimum possible initial value if initial weights are randomized. When set, this will override
Weight Initialize = RANDOMIZED${\mathbf{Weight\; Initialize}}=\mathrm{RANDOMIZED}$ or
MAXIMUM$\mathrm{MAXIMUM}$, and as such these must be set afterwards if so desired.
Constraint:
W_{min} ≤ r ≤ W_{max}${W}_{\mathit{min}}\le r\le {W}_{\mathit{max}}$.
Weight Initialize a$a$Default = MAXIMUM$\text{}=\mathrm{MAXIMUM}$Determines how the initial weights are distributed.
 INITIAL
 All weights are initialized at the initial weight, W_{ini}${W}_{\mathit{ini}}$, if set. If Weight Initial has not been set, this will be the maximum weight, W_{max}${W}_{\mathit{max}}$.
 MAXIMUM
 All weights are initialized at the maximum weight, W_{max}${W}_{\mathit{max}}$.
 RANDOMIZED
 Weights are uniformly distributed in (W_{min},W_{max})$({W}_{\mathit{min}},{W}_{\mathit{max}})$ or (W_{ini},W_{max})$({W}_{\mathit{ini}},{W}_{\mathit{max}})$ if Weight Initial has been set.
Weight Maximum r$r$Default = 1.0$\text{}=1.0$The maximum particle weight, W_{max}${W}_{\mathit{max}}$.
Constraint:
1.0 ≥ r ≥ W_{min}$1.0\ge r\ge {W}_{\mathit{min}}$ (If W_{ini}${W}_{\mathit{ini}}$ has been set then 1.0 ≥ r ≥ W_{ini}$1.0\ge r\ge {W}_{\mathit{ini}}$.)
Weight Minimum r$r$Default = 0.1$\text{}=0.1$The minimum achievable weight of any particle, W_{min}${W}_{\mathit{min}}$. Once achieved, no further weight reduction is possible.
Constraint:
0.0 ≤ r ≤ W_{max}$0.0\le r\le {W}_{\mathit{max}}$ (If W_{ini}${W}_{\mathit{ini}}$ has been set then 0.0 ≤ r ≤ W_{ini}$0.0\le r\le {W}_{\mathit{ini}}$.)
Weight Reset a$a$Default = MAXIMUM$\text{}=\mathrm{MAXIMUM}$Determines how particle weights are reinitialized.
 INITIAL
 Weights are reinitialized at the initial weight if set. If 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 (W_{min},W_{max})$({W}_{\mathit{min}},{W}_{\mathit{max}})$ or (W_{ini},W_{max})$({W}_{\mathit{ini}},{W}_{\mathit{max}})$ if Weight Initial has been set.
Weight Value r$r$Default = 0.01$\text{}=0.01$The constant
W_{val}${W}_{\mathit{val}}$ used with
Weight Decrease = INTEREST${\mathbf{Weight\; Decrease}}=\mathrm{INTEREST}$.
Constraint:
0.0 ≤ r ≤ (1/3)$0.0\le r\le \frac{1}{3}$.
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