Title: Model-Independent Particle Swarm Optimisation for Environmental Models
Title: Model-Independent Particle Swarm Optimisation for Environmental Models
Version: 0.1-58-27
Version: 0.1-58-28
Date: 2012-11-20
Date: 2012-11-21
Authors@R: c(person("Mauricio", "Zambrano-Bigiarini", email="mzb.devel@gmail.com", role=c("aut","cre"), comment="feel free to write me in English, Spanish or Italian"), person("Rodrigo", "Rojas", email="Rodrigo.RojasMujica@gmail.com", role=c("ctb")) )
Authors@R: c(person("Mauricio", "Zambrano-Bigiarini", email="mzb.devel@gmail.com", role=c("aut","cre"), comment="feel free to write me in English, Spanish or Italian"), person("Rodrigo", "Rojas", email="Rodrigo.RojasMujica@gmail.com", role=c("ctb")) )
Description: This package implements a state-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable), with a special focus on the calibration of environmental models. hydroPSO is model-independent, allowing the user to easily interface any model code with the calibration engine (PSO). It includes a series of controlling options and PSO variants to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. Bugs reports/comments/questions are very welcomed.
Description: This package implements a state-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable), with a special focus on the calibration of environmental models. hydroPSO is model-independent, allowing the user to easily interface any model code with the calibration engine (PSO). It includes a series of controlling options and PSO variants to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. Bugs reports/comments/questions are very welcomed.
# Description: The Sphere test function is one of the most simple test functions available in the specialized literature. This unimodal and separable test function can be scaled up to any number of variables. It belongs to a family of functions called quadratic functions and only has one optimum in the point o = (0,...,0). The search range commonly used for the Sphere function is [−100, 100] for each decision variable.
# Description: The generalized Rastrigin test function is non-convex and multimodal. It has several local optima arranged in a regular lattice, but it only has one global optimum located at the point \preformatted{o=(0,...,0)}. The search range for the Rastrigin function is [-5.12, 5.12] in each variable. This function is a fairly difficult problem due to its large search space and its large number of local minima
# Description: The Griewank test function is multimodal and non-separable, with has several local optima within the search region defined by [-600, 600]. It is similar to the Rastrigin function, but the number of local optima is larger in this case. It only has one global optimum located at the point \kbd{o=(0,...,0)}. While this function has an exponentially increasing number of local minima as its dimension increases, it turns out that a simple multistart algorithm is able to detect its global minimum more and more easily as the dimension increases (Locatelli, 2003)
# Description: The Ackley test function is multimodal and separable, with several local optima that, for the search range [-32, 32], look more like noise, although they are located at regular intervals. The Ackley function only has one global optimum located at the point o=(0,...,0).
@@ -54,9 +54,40 @@ numeric shifting vector to be used, with the same length of \code{x}
...
@@ -54,9 +54,40 @@ numeric shifting vector to be used, with the same length of \code{x}
numeric with the bias to be imposed
numeric with the bias to be imposed
}
}
}
}
%\details{
\details{
%\code{Ackley}
The \bold{Ackley} test function is multimodal and separable, with several local optima that, for the search range [-32, 32], look more like noise, although they are located at regular intervals. The Ackley function only has one global optimum located at the point \kbd{o=(0,...,0)}. It is defined by:
The generalized \bold{Rastrigin} test function is non-convex, multimodal and additively separable. It has several local optima arranged in a regular lattice, but it only has one global optimum located at the point \kbd{o=(0,...,0)}. The search range for the Rastrigin function is [-5.12, 5.12] in each variable. This function is a fairly difficult problem due to its large search space and its large number of local minima. It is defined by:
The \bold{Griewank} test function is multimodal and non-separable, with has several local optima within the search region defined by [-600, 600]. It is similar to the Rastrigin function, but the number of local optima is larger in this case. It only has one global optimum located at the point \kbd{o=(0,...,0)}. While this function has an exponentially increasing number of local minima as its dimension increases, it turns out that a simple multistart algorithm is able to detect its global minimum more and more easily as the dimension increases (Locatelli, 2003). It is defined by:
The \bold{Rosenbrock} function is non-convex, unimodal and non-separable. It is also known as \emph{Rosenbrock's valley} or \emph{Rosenbrock's banana} function. The global minimum is inside a long, narrow, parabolic shaped flat valley. To find the valley is trivial. To converge to the global minimum, however, is difficult. It only has one optimum located at the point \kbd{o=(1,...,1)}. It is a quadratic function, and its search range is [-30, 30] for each variable. It is defined by:
The \bold{Sphere} test function is one of the most simple test functions available in the specialized literature. This unimodal and additively separable test function can be scaled up to any number of variables. It belongs to a family of functions called quadratic functions and only has one optimum in the point \kbd{o=(0,...,0)}. The search range commonly used for the Sphere function is [-100, 100] for each decision variable. It is defined by:
Griewank: \cite{Locatelli, M. 2003. A note on the griewank test function, Journal of Global Optimization, 25 (2), 169-174, doi:10.1023/A:1021956306041}
