hydroPSO.Rd

%% File hydroPSO.Rd
%% Part of the hydroPSO R package, http://www.rforge.net/hydroPSO/ ; 
%%                                 http://cran.r-project.org/web/packages/hydroPSO
%% Copyright 2011-2012 Mauricio Zambrano-Bigiarini & Rodrigo Rojas
%% Distributed under GPL 2 or later

\name{hydroPSO}
\alias{hydroPSO}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Enhanced Particle Swarm Optimisation algorithm
}
\description{
Particle Swarm Optimisation algorithm to calibrate environmental models. It includes a series of controlling options and PSO variants to improve the performance of the algorithm and customize it to different calibration problems
}
\usage{
hydroPSO(par, fn= "hydromod", ..., method=c("pso", "ipso", "fips", "wfips"),
         lower=-Inf, upper=Inf, control=list(), 
         model.FUN=NULL, model.FUN.args=list() )
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{par}{
OPTIONAL. numeric with a first guess for the parameters to be optimised (\env{n}, number of parameters and dimension of the solution space) \cr
All the particles are randomly initialised according to the value of \code{Xini.type}. If the user provides \env{m} parameter sets for \code{par}, they are used to overwrite the first \env{m} parameter sets randomly defined according to the value of \code{Xini.type}. If some elements in \code{par} are non finite (lower than \code{lower} or larger than \code{upper}) they are ignored \cr
}
  \item{fn}{
character, name of a valid R function to be optimised (minimized or maximized) or the character value \sQuote{'hydromod'}. \cr
-) When \code{fn!='hydromod'}, the first argument of \code{fn} has to be a vector of parameters over which optimisation is to take place. It should return a scalar result. When \code{fn!='hydromod'} the algorithm uses the value(s) returned by \code{fn} as both model output and its corresponding goodness-of-fit measure. \cr
-) When \code{fn=='hydromod'} the algorithm will optimise the model defined by \code{model.FUN} and \code{model.args}, which are used to extract the values simulated by the model and to compute its corresponding goodness-of-fit measure. 
}
\item{\dots}{
OPTIONAL. Only used when \code{fn!='hydromod'}. \cr
further arguments to be passed to \code{fn}.
}
  \item{method}{
character, variant of the PSO algorithm to be used. Valid values are in \code{c('pso', 'ipso', 'fips', 'wfips')}: \cr

\kbd{pso}: at each iteration particles are attracted to its own best-known personal and to the best-known global position. Each particle is connected to a neighbourhood of particles depending on the \code{topology} value \cr
\kbd{ipso}: at each iteration particles in the swarm are rearranged in descending order according to their goodness-of-fit and the best \code{ngbest} particles are used to modify particles' position and velocity (see Zhao, 2006). Each particle is connected to a neighbourhood of particles depending on the \code{topology} value \cr
\kbd{fips}: at each iteration ALL particles contribute to modify the particles' position and velocity (see Mendes et al., 2004). Each particle is connected to a neighbourhood of particles depending on the \code{topology} value \cr
\kbd{wfips}: same implementation as \kbd{fips} method, but the contribution of each particle is weighted according to their goodness-of-fit value (see Mendes et al., 2004) \cr
By default \code{method=pso}
}
  \item{lower}{
numeric, lower boundary for each parameter \cr
Note for \code{\link[stats]{optim}} users: in \kbd{hydroPSO} the length of \code{lower} and \code{upper} are used to defined the dimension of the solution space
}
  \item{upper}{
numeric, upper boundary for each parameter \cr
Note for \code{\link[stats]{optim}} users: in \kbd{hydroPSO} the length of \code{lower} and \code{upper} are used to defined the dimension of the solution space
}
  \item{control}{
a list of control parameters. See \sQuote{Details}
}
  \item{model.FUN}{
OPTIONAL. Used only when \code{fn='hydromod'} \cr
character, valid R function representing the model code to be calibrated/optimised
} 
  \item{model.FUN.args}{
OPTIONAL. Used only when \code{fn='hydromod'} \cr
list with the arguments to be passed to \code{model.FUN}
} 

}
\details{
By default the hydroPSO function performs minimization of \code{fn}, but it will maximize \code{fn} if \code{MinMax='max'} \cr

The default control arguments in hydroPSO implements the Standard PSO 2007 - SPSO2007 (see Clerc 2005; Clerc et al., 2010). At the same time, hydroPSO function provides options for clamping the maximal velocity, regrouping strategy when premature convergence is detected, time-variant acceleration coefficients, time-varying maximum velocity, (non-)linear / random / adaptive / best-ratio inertia weight definitions, random or LHS initialization of positions and velocities, synchronous or asynchronous update, 4 alternative neighbourhood topologies among others

The \code{control} argument is a list that can supply any of the following components:
  \describe{    
    \item{drty.in}{
OPTIONAL. Used only when \code{fn='hydromod'} \cr
character, name (without path) of the directory storing the input files required for PSO, i.e. \sQuote{ParamRanges.txt} and \sQuote{ParamFiles.txt}
}
  \item{drty.out}{
character, path to the directory storing the output files generated by hydroPSO
}
  \item{param.ranges}{
OPTIONAL. Used only when \code{fn='hydromod'} \cr
character, name of the file storing the desired range of variation of each parameter
} 
  \item{digits}{
OPTIONAL. Used only when \code{write2disk=TRUE} \cr
numeric, number of significant digits used for writing the outputs in scientific notation
}
  \item{MinMax}{
character, indicates whether a maximization or minimization problem needs to be solved. Valid values are in: \code{c('min', 'max')}. Default value is \kbd{min}
}
  \item{npart}{
numeric, number of particles in the swarm
}
  \item{maxit}{
numeric, maximum number of iterations
}
  \item{maxfn}{
numeric, maximum number of function evaluations. Default value is \code{+Inf}
}
  \item{c1}{
numeric, cognitive acceleration coefficient. Encourages the exploration of the solution space and reflects how much the particle is influenced by its own best-known position
}
  \item{c2}{
numeric, social acceleration coefficient. Encourages the exploitation of the current global best and reflects how much the particle is influenced by the best-known optimum of the swarm 
}
  \item{use.CF}{
logical, indicates if the Clerc's Constriction Factor (see Clerc, 1999; Eberhart and Shi, 2000; Clerc and Kennedy, 2002) is used to avoid swarm explosion
}
  \item{lambda}{
numeric in [0,1], represents a percentage to limit the maximum velocity (Vmax) for each dimension, which is computed as \code{vmax = lambda*(Xmax-Xmin)}
}
  \item{abstol}{
numeric, absolute convergence tolerance. The algorithm stops if \code{gbest <= abstol} (minimisation problems) OR when \code{gbest >= abstol} (maximisation problems) \cr
By default it is set to \code{-Inf} or \code{+Inf} for minimisation or maximisation problems, respectively
}
  \item{reltol}{
numeric, relative convergence tolerance. The algorithm stops if the absolute difference between the best \sQuote{personal best} in the current iteration and the best \sQuote{personal best} in the previous iteration is lower or equal to \code{reltol}. Defaults to \code{sqrt(.Machine$double.eps)}, typically, about 1e-8
}
  \item{Xini.type}{
character, indicates how to initialise the particles' positions in the swarm within the ranges defined by \code{lower} and \code{upper}. Valid values are: \cr
-) \kbd{lhs}: Latin Hypercube initialisation of positions, using \code{npart} number of strata to divide each parameter range. \bold{It requires the \pkg{lhs} package}  \cr
-) \kbd{random}: random initialisation of positions within \code{lower} and \code{upper} \cr
By default \code{Xini.type=lhs}
}
  \item{Vini.type}{
character, indicates how to initialise the particles' velocities in the swarm. Valid values are: \cr
-) \kbd{zero}: all the particles are initialised with zero velocity \cr
-) \kbd{random}: random initialisation of velocities within \code{lower} and \code{upper} using the \sQuote{half-diff} method (\samp{Vini=[U(lower, upper)-Xini]/2}) (see Clerc, 2010) \cr
-) \kbd{lhs}: Latin Hypercube initialisation of velocities using \code{npart} number of strata to divide each parameter range and the \kbd{half-diff} method (\samp{Vini=[LHS(lower, upper)-Xini]/2}) (see Clerc, 2010). \bold{It requires the \pkg{lhs} package} \cr
By default \code{Vini.type=lhs}
}
  \item{best.update}{
character, indicates how (when) to update the global/neighbourhood and personal best. Valid values are: \cr
-)\kbd{sync}: the update is made synchronously, i.e. after computing the position and goodness-of-fit for ALL the particles in the swarm. This is the DEFAULT option\cr
-)\kbd{async}: the update is made asynchronously, i.e. after computing the position and goodness-of-fit for EACH individual particle in the swarm
}
  \item{random.update}{
OPTIONAL. Only used when \code{best.update='async'}. \cr
logical, if \code{TRUE} the particles are processed in random order to update their personal best and the global/neighbourhood best. By default \code{random.update=TRUE}
}
  \item{boundary.wall}{
character, indicates the type of boundary condition to be applied during optimisation. Valid values are in \code{c('reflecting', 'damping', 'absorbing', 'invisible')} \cr

Experience has shown that Clerc's constriction factor and the inertia weights do not always confine the particles within the solution space. To address this problem, Robinson and Rahmat-Samii (2004) and Huang and Mohan (2005) propose different boundary conditions, namely, \kbd{reflecting}, \kbd{damping}, \kbd{absorbing} and \kbd{invisible} to define how particles are treated when reaching the boundary of the searching space (see Robinson and Rahmat-Samii (2004) and Huang and Mohan (2005) for further details)
}
  \item{topology}{
character, indicates the neighbourhood topology used in hydroPSO. Valid values are in \code{c('gbest', 'lbest', 'vonNeumann', 'random')}: \cr
\kbd{gbest}: every particle is connected to each other and, hence the global best influences all particles in the swarm. This is also termed \samp{star} topology, and it is generally assumed to have a fast convergence but is more vulnerable to the attraction to sub-optimal solutions (see Kennedy, 1999; Kennedy and Mendes, 2002, Schor et al., 2010) \cr
\kbd{lbest}: each particle is connected to its \code{K} immediate neighbours only. This is also termed \samp{circles} or \samp{ring} topology, and generally the swarm will converge slower than the \kbd{gbest} topology but it is less vulnerable to sub-optimal solutions (see Kennedy, 1999; Kennedy and Mendes, 2002) \cr
\kbd{vonNeumann}: each particle is connected to its \code{K=4} immediate neighbours only. This topology is more densely connected than \samp{lbest} but less densely than \samp{gbest}, thus, showing some parallelism with \samp{lbest} but benefiting from a bigger neighbourhood (see Kennedy and Mendes, 2003) \cr
\kbd{random}: the random topology is a special case of \samp{lbest} where connections among particles are randomly modified after an iteration showing no improvement in the global best (see Clerc, 2005; Clerc, 2010) \cr
By default \code{topology=gbest}
}
  \item{K}{
OPTIONAL. Only used when \code{topology} is in \code{c(lbest, vonNeumann, random)} \cr
numeric, neighbourhood size, i.e. the number of informants for each particle (including the particle itself) to be considered in the computation of their personal best \cr
When \code{topology=lbest} \code{K} MUST BE an even number in order to consider the same amount of neighbours to the left and the right of each particle. \cr
As special case, \code{K} could be equal to \code{npart} \cr
By default \code{K=3}
}
  \item{iter.ini}{
OPTIONAL. Only used when \code{topology=='lbest'} \cr
numeric, number of iterations for which the \kbd{gbest} topology will be used before using the \kbd{lbest} topology for the computation of the personal best of each particle\cr
This option aims at making faster the identification of the global zone of attraction \cr
By default \code{iter.ini=0}
}
  \item{ngbest}{
OPTIONAL. Only used when \code{method=='ipso'} \cr
numeric, number of particles considered in the computation of the global best \cr
By default \code{ngbest=4} (see Zhao, 2006)
}
  \item{use.IW}{
logical, indicates if an inertia weight (\env{w}) will be used to avoid swarm explosion, i.e. particles flying around their best position without converging into it (see Shi and Eberhart, 1998)
}
  \item{IW.type}{
OPTIONAL. Used only when \code{use.IW= TRUE} \cr
character, defines how the inertia weight \env{w} will vary along iterations. Valid values are: \cr
-)\kbd{linear}: \env{w} varies linearly between the initial and final values specified in \code{IW.w} (see Shi and Eberhart, 1998; Zheng et al., 2003) \cr
-)\kbd{non-linear}: \env{w} varies non-linearly between the initial and final values specified in \code{IW.w} with exponential factor \kbd{IW.exp} (see Chatterjee and Siarry, 2006) \cr
-)\kbd{runif}: \env{w} is a uniform random variable in the range \env{[w.min, w.max]} specified in \code{IW.w}. It is a generalisation of the weight proposed in Eberhart and Shi (2001b) \cr
-)\kbd{aiwf}: adaptive inertia weight factor, where the inertia weight is varied adaptively depending on the goodness-of-fit values of the particles (see Liu et al., 2005) \cr
-)\kbd{GLratio}: \env{w} varies according to the ratio between the global best and the average of the particle's local best (see Arumugam and Rao, 2008) \cr
By default \code{IW.type=linear}
}
  \item{IW.w}{
OPTIONAL. Used only when \code{use.IW= TRUE \& IW.type!='GLratio'} \cr
numeric, value of the inertia weight(s) (\env{w} or \env{[w.ini, w.fin]}). It can be a single number which is used for all iterations, or can be a vector of length 2 with the initial and final values (in that order) that \env{w} will take along the iterations
}
  \item{IW.exp}{
OPTIONAL. Used only when \code{use.IW= TRUE} AND \code{IW.type= 'non-linear'} \cr
numeric, non-linear modulation index (see Chatterjee and Siarry, 2006) \cr
When \code{IW.type='linear'}, \code{IW.exp} is set to 1
}
 \item{use.TVc1}{
logical, indicates if the cognitive acceleration coefficient \code{c1} will have a time-varying value instead of a constant one provided by the user (see Ratnaweera et al. 2004) \cr
By default \code{use.TVc1=TRUE}
}
  \item{TVc1.type}{
character, required only when \code{use.TVc1 = TRUE}. Valid values are: \cr
-)\kbd{linear}: \env{c1} varies linearly between the initial and final values specified in \code{TVc1.rng} (see Ratnaweera et al., 2004) \cr
-)\kbd{non-linear}: \env{c1} varies non-linearly between the initial and final values specified in \code{TVc1.rng}. Proposed by the authors of hydroPSO taking into account the work of Chatterjee and Siarry (2006) for the inertia weight \cr
-)\kbd{GLratio}: \env{c1} varies according to the ratio between the global best and the average of the particle's local best (see Arumugam and Rao, 2008) \cr
By default \code{TVc1.type=linear}
}
  \item{TVc1.rng}{
OPTIONAL. Used only when \code{use.TVc1= TRUE \& TVc1.type!='GLratio'} \cr
numeric, initial and final values for the cognitive acceleration coefficient \env{[c1.ini, c1.fin]} (in that order) along the iterations
}
  \item{TVc1.exp}{
OPTIONAL. Used only when \code{use.TVc1= TRUE} AND \code{TVc1.type= 'non-linear'} \cr
numeric, non-linear modulation index \cr
When \code{TVc1.exp} is equal to 1, \code{TVc1} corresponds to the improvement proposed by Ratnaweera et al., (2004), whereas when \code{TVc1.exp} is different from one, no reference has been found in literature by the authors, but it was included as an option based on the work of Chatterjee and Siarry (2006) for the inertia weight \cr
When \code{TVc1.type= linear} \code{TVc1.exp} is automatically set to 1
}
 \item{use.TVc2}{
logical, indicates whether the social acceleration coefficient \code{c2} will have a time-varying value or a constant one provided by the user (see Ratnaweera et al. 2004) \cr
By default \code{use.TVc2=FALSE}
}
  \item{TVc2.type}{
character, required only when \code{use.TVc2=TRUE}. Valid values are: \cr
-)\kbd{linear}: \env{c2} varies linearly between the initial and final values specified in \code{TVc2.rng} (see Ratnaweera et al. 2004) \cr
-)\kbd{non-linear}: \env{c2} varies non-linearly between the initial and final values specified in \code{TVc2.rng}. Proposed by the authors of hydroPSO taking into account the work of Chatterjee and Siarry (2006) for the inertia weight \cr
By default \code{TVc2.type=linear}
}
  \item{TVc2.rng}{
OPTIONAL. Used only when \code{use.TVc2=TRUE} \cr
numeric, initial and final values for the social acceleration coefficient \env{[c2.ini, c2.fin]} (in that order) along the iterations
}
  \item{TVc2.exp}{
OPTIONAL. Used only when \code{use.TVc2= TRUE} AND \code{TVc2.type='non-linear'} \cr
numeric, non-linear modulation index \cr
When \code{TVc2.exp} is equal to 1, \code{TVc2} corresponds to the improvement proposed by Ratnaweera et al., 2004, whereas when \code{TVc2.exp} is different from one, no reference has been found in literature by the authors, but it was included as an option based on the work of Chatterjee and Siarry (2006) for the inertia weight \cr
When \code{TVc2.type= linear} \code{TVc2.exp} is automatically set to 1
}
 \item{use.TVlambda}{
logical, indicates whether the percentage to limit the maximum velocity \code{lambda} will have a time-varying value or a constant value provided by the user. Proposed by the authors of hydroPSO based on the work of Chatterjee and Siarry (2006) for the inertia weight\cr
By default \code{use.TVlambda=TRUE}
}
  \item{TVlambda.type}{
character, required only when \code{use.TVlambda=TRUE}. Valid values are: \cr
-)\kbd{linear}: \env{TVvmax} varies linearly between the initial and final values specified in \code{TVlambda.rng} \cr
-)\kbd{non-linear}: \env{TVvmax} varies non-linearly between the initial and final values specified in \code{TVlambda.rng} \cr
By default \code{TVlambda.type=linear}
}
  \item{TVlambda.rng}{
OPTIONAL. Used only when \code{use.TVlambda=TRUE} \cr
numeric, initial and final values for the percentage to limit the maximum velocity \env{[TVlambda.ini, TVlambda.fin]} (in that order) along the iterations
}
  \item{TVlambda.exp}{
OPTIONAL. only required when \code{use.TVlambda= TRUE} AND \code{TVlambda.type='non-linear'} \cr
numeric, non-linear modulation index \cr
When \code{TVlambda.type='linear'} \code{TVlambda.exp} is automatically set to 1 
}
  \item{use.RG}{
logical, indicates if the swarm should be regrouped when premature convergence is detected. When \code{use.RG=TRUE} the swarm is regrouped in a search space centred around the current global best. This updated search space is hoped to be both small enough for efficient search and large enough to allow the swarm to escape from stagnation (see Evers and Ghalia, 2009)
}
  \item{RG.thr}{
ONLY required when \code{use.RG=TRUE} \cr
numeric, positive number representing the \kbd{stagnation threshold} used to decide whether the swarm has to be regrouped or not. See Evers and Galia (2009) for further details \cr
Regrouping occurs when the \kbd{normalised swarm radius} is less than \code{RG.thr}
}
  \item{RG.r}{
ONLY required when \code{use.RG=TRUE}. \cr
numeric, positive number representing the \kbd{regrouping factor}, which is used to regroup the swarm in a search space centred around the current global best (see Evers and Galia, 2009 for further details)
}
  \item{RG.miniter}{
ONLY required when \code{use.RG=TRUE} \cr
numeric, minimum number of iterations needed before regrouping
}
%%  \item{use.DS}{
%%CPSO
%%}
%%  \item{DS.r}{
%%     ~~Describe \code{DS.r} here~~
%%}
%%  \item{DS.tol}{
%%     ~~Describe \code{DS.tol} here~~
%%}
%%  \item{DS.dmin}{
%%     ~~Describe \code{DS.dmin} here~~
%%}
  \item{plot}{
logical, indicatesif a two-dimensional plot with the particles' position will be drawn after each iteration. For high dimensional functions, only the first two dimensions of all the particles are plotted
}
  \item{out.with.pbest}{
logical, indicates if the best parameter values for each particle and their goodness-of-fit will be included in the output of the algorithm \cr
By default \code{out.with.pbest=FALSE}
}
  \item{out.with.fit.iter}{
logical, indicates if the goodness-of-fit of each particle for each iteration will be included in the output of the algorithm \cr
By default \code{out.with.fit.iter=FALSE}
}
  \item{write2disk}{
logical, indicates if the output files will be written to the disk 
}
  \item{verbose}{
logical, indicates if progress messages are to be printed
} 
  \item{REPORT}{
OPTIONAL. Used only when \code{verbose=TRUE} \cr
The frequency of report messages printed to the screen. Default to every 10 iterations
} 
  
}
}
\value{
A list, compatible with the output from \code{\link[stats]{optim}}, with components:
\item{par}{
optimum parameter set found    
}
\item{value}{
value of \code{fn} corresponding to \code{par}
}
\item{counts}{
three-element vector containing the number of function evaluations, number of iterations, and number of regrouping
}
\item{convergence}{
integer code where \code{0} indicates that the algorithm terminated by reaching the absolute tolerance, otherwise:
\describe{
 \item{\code{1}:}{relative tolerance reached}
 \item{\code{2}:}{maximum number of function evaluations reached}
 \item{\code{3}:}{maximum number of iterations reached}
}
}
\item{message}{character string giving human-friendly information about \code{convergence}
}
}
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\cite{Kennedy, J.; Mendes, R.; , Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms. Soft Computing in Industrial Applications, 2003. SMCia/03. Proceedings of the 2003 IEEE International Workshop on , vol., no., pp. 45- 50, 23-25 June 2003. doi: 10.1109/SMCIA.2003.1231342}

\cite{Kennedy, J.; Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on , vol.3, no., pp.3 vol. (xxxvii+2348), 1999. doi: 10.1109/CEC.1999.785509}
     
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\cite{Clerc, M. Particle Swarm Optimization. ISTE, 2005}

\cite{Clerc, M. From Theory to Practice in Particle Swarm Optimization, Handbook of Swarm Intelligence, Springer Berlin Heidelberg, 3-36, Eds: Panigrahi, Bijaya Ketan, Shi, Yuhui, Lim, Meng-Hiot, Hiot, Lim Meng, and Ong, Yew Soon, 2010, doi: 10.1007/978-3-642-17390-5_1}

\cite{Clerc, M., Stagnation Analysis in Particle Swarm Optimisation or what happens when nothing happens. Technical Report. 2006. \url{http://hal.archives-ouvertes.fr/hal-00122031}}

\cite{Clerc, M. Standard Particle Swarm. 2011. (SPSO-2007, SPSO-2011). \url{clerc.maurice.free.fr/pso/SPSO_descriptions.pdf}. Last visited [25-Jan-2012]}

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\cite{Yong-Ling Zheng; Long-Hua Ma; Li-Yan Zhang; Ji-Xin Qian. On the convergence analysis and parameter selection in particle swarm optimization. Machine Learning and Cybernetics, 2003 International Conference on , vol.3, no., pp. 1802-1807 Vol.3, 2-5 Nov. 2003. doi: 10.1109/ICMLC.2003.1259789}

\cite{Zhao, B.  An Improved Particle Swarm Optimization Algorithm for Global Numerical Optimization. In Proceedings of International Conference on Computational Science (1). 2006, 657-664}

\cite{Neighborhood Topologies, \url{http://tracer.uc3m.es/tws/pso/neighborhood.html}. Last visited [15-Feb-2012] }
}
\author{
Mauricio Zambrano-Bigiarini, \email{mzb.devel@gmail.com}
}
\note{
Note for \code{\link[stats]{optim}} users: \cr
1) In \kbd{hydroPSO}, \code{par} may be omitted. If not omitted, the  \env{m} parameter sets provided by the user for \code{par} are used to overwrite the first \env{m} parameter sets randomly defined according to the value of \code{Xini.type} \cr
2) In \kbd{hydroPSO} the length of \code{lower} and \code{upper} are used to define the dimension of the solution space (not the length of \code{par})
}

%% ~Make other sections like Warning with \section{Warning }{....} ~

\seealso{
\code{\link[stats]{optim}}
}
\examples{
# Number of dimensions to be optimised
nparam <- 5

\dontrun{
# Setting the home directory of the user as working directory 
setwd("~")
   
# Setting the seed          
set.seed(100)

hydroPSO(
        fn="rastrigrin",
        lower=rep(-5.12, nparam), upper=rep(5.12, nparam),    
        control=list(    
                    MinMax="min",
                    npart=2*nparam, 
            
                    use.TVlambda= TRUE, TVlambda.type= "linear", 
                    TVlambda.rng= c(1, 0.5), TVlambda.exp= 1,
            
                    topology="gbest",            
                    write2disk=TRUE
                    ) # control
        ) # hydroPSO

# Plotting the results
plot_results(MinMax="min")
} # dontrun END

}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{optimize}
\keyword{files}