pboptim: Population Based Search

In ToshihiroIguchi/pboptim: Population Based Search

Description

Pboptim is an R package that performs multiple population based search. Algorithms Differential Evolution Optimization, Particle Swarm Optimization, Genetic Algorithms and Random Search can be used. "pboptim" depends on "DEoptim", "pso" and "GA" packages.

Usage

pboptim(fn ,lower, upper, initialpar = NULL,
method = c("DEO", "PSO", "GA"),
population = 20, generation = 10,
trace = TRUE, maximize = FALSE,
DEOcontrol = list(strategy = 2, bs = FALSE, CR = 0.5, F = 0.8, p = 0.2, c = 0),
PSOcontrol = list(s = floor(10+2*sqrt(length(pso_par))),
k = 3, w = 0.7213, c.p = 1.193, type = "SPSO2007"),
GAcontrol = list(pcrossover = 0.8,pmutation = 0.1,
elitism = base::max(1, round(population * 0.05))),

)

Arguments

fn	A function to be minimized (or maximized).
lower	Lower bounds on the variables.
upper	Upper bounds on the variables.
initialpar	A vector representing the initial value. Default is NULL.
method	A vector that specifies the optimization technique. Default is c("DEO", "PSO", "GA"). RF can also be selected.
population	The population size.
generation	The maximum number of iterations.
trace	Show progress of optimization calculation.
maximize	For maximization, specify TRUE. Default is FALSE.
strategy	defines the Differential Evolution strategy used in the optimization procedure: 1: DE / rand / 1 / bin (classical strategy) 2: DE / local-to-best / 1 / bin (default) 3: DE / best / 1 / bin with jitter 4: DE / rand / 1 / bin with per-vector-dither 5: DE / rand / 1 / bin with per-generation-dither 6: DE / current-to-p-best / 1 any value not above: variation to DE / rand / 1 / bin: either-or-algorithm. Default strategy is currently 2.
bs	if FALSE then every mutant will be tested against a member in the previous generation, and the best value will proceed into the next generation (this is standard trial vs. target selection). If TRUE then the old generation and NP mutants will be sorted by their associated objective function values, and the best NP vectors will proceed into the next generation (best of parent and child selection). Default is FALSE.
CR	crossover probability from interval [0,1]. Default to 0.5.
F	differential weighting factor from interval [0,2]. Default to 0.8.
p	when strategy = 6, the top (100 * p)% best solutions are used in the mutation. p must be defined in (0,1].
c	c controls the speed of the crossover adaptation. Higher values of c give more weight to the current successful mutations. c must be defined in (0,1].

References

Cortez, Paulo. Modern optimization with R. Springer, 2014.

Examples

#Population Based Search
result <- pboptim(function(x){x[1]^2+x[2]^2},
                method = c("DEO", "PSO", "GA"),
                initialpar = c(1, 1),
                lower = c(-2,-2), upper = c(2,2),
                population = 20, generation = 100,
                maximize = FALSE)

#View generation vs value
plot(result)

#summary
summary(result)

ToshihiroIguchi/pboptim documentation built on May 21, 2019, 2:20 p.m.