Nothing
JDEoptim <- function(lower, upper, fn, constr = NULL, meq = 0, eps = 1e-5,
NP = 10*length(lower), Fl = 0.1, Fu = 1,
tau_F = 0.1, tau_CR = 0.1, tau_pF = 0.1,
jitter_factor = 0.001,
tol = 1e-15, maxiter = 200*length(lower), fnscale = 1,
compare_to = c("median", "max"),
add_to_init_pop = NULL,
trace = FALSE, triter = 1,
details = FALSE, ...)
# Copyright 2013, 2014, 2016, 2023, Eduardo L. T. Conceicao
# Available under the GPL (>= 2)
{
handle.bounds <- function(x, u) {
# Check feasibility of bounds and enforce parameters limits
# by a deterministic variant of bounce-back resetting
# (also known as midpoint target/base)
# Price, KV, Storn, RM, and Lampinen, JA (2005)
# Differential Evolution: A Practical Approach to Global Optimization.
# Springer, p 206
bad <- x > upper
x[bad] <- 0.5*(upper[bad] + u[bad])
bad <- x < lower
x[bad] <- 0.5*(lower[bad] + u[bad])
x
}
performReproduction <- function() { # Mutate/recombine
ignore <- runif(d) > CRtrial
if (all(ignore)) # ensure that trial gets at least
ignore[sample(d, 1)] <- FALSE # one mutant parameter
# Source for trial is the base vector plus weighted differential
trial <- if (runif(1) <= pFtrial)
X.base + Ftrial*(X.r1 - X.r2)
else X.base + 0.5*(Ftrial + 1)*(X.r1 + X.r2 - 2*X.base)
# or trial parameter comes from target vector X.i itself.
trial[ignore] <- X.i[ignore]
trial
}
which.best <- if (!is.null(constr))
function(x) {
ind <- TAVpop <= mu
if (all(ind))
which.min(x)
else if (any(ind))
which(ind)[which.min(x[ind])]
else which.min(TAVpop)
}
else which.min
# Check input parameters
compare_to <- match.arg(compare_to)
stopifnot(length(upper) == length(lower),
length(lower) > 0, is.numeric(lower), is.finite(lower),
length(upper) > 0, is.numeric(upper), is.finite(upper),
lower <= upper,
is.function(fn))
if (!is.null(constr))
stopifnot(is.function(constr),
length(meq) == 1, meq == as.integer(meq), meq >= 0,
is.numeric(eps), is.finite(eps), eps > 0,
length(eps) == 1 || length(eps) == meq)
stopifnot(length(NP) == 1, NP == as.integer(NP), NP >= 0,
length(Fl) == 1, is.numeric(Fl),
length(Fu) == 1, is.numeric(Fu),
Fl <= Fu)
stopifnot(length(tau_F) == 1, is.numeric(tau_F), 0 <= tau_F, tau_F <= 1,
length(tau_CR) == 1, is.numeric(tau_CR), 0 <= tau_CR, tau_CR <= 1,
length(tau_pF) == 1, is.numeric(tau_pF), 0 <= tau_pF, tau_pF <= 1)
if (!is.null(jitter_factor))
stopifnot(length(jitter_factor) == 1,
is.numeric(jitter_factor),
is.finite(jitter_factor))
stopifnot(length(tol) == 1, is.numeric(tol), is.finite(tol),
length(maxiter) == 1, maxiter == as.integer(maxiter),
maxiter >= 0,
length(fnscale) == 1, is.numeric(fnscale),
is.finite(fnscale), fnscale > 0)
if (!is.null(add_to_init_pop))
stopifnot(NROW(add_to_init_pop) == length(lower),
is.numeric(add_to_init_pop),
is.finite(add_to_init_pop),
add_to_init_pop >= lower,
add_to_init_pop <= upper)
stopifnot(length(trace) == 1, is.logical(trace), !is.na(trace),
length(triter) == 1, triter == as.integer(triter), triter >= 1,
length(details) == 1, is.logical(details), !is.na(details))
child <- if (is.null(constr)) { # Evaluate/select
expression({
ftrial <- fn1(trial)
if (ftrial <= fpop[i]) {
pop[, i] <- trial
fpop[i] <- ftrial
F[, i] <- Ftrial
CR[i] <- CRtrial
pF[i] <- pFtrial
}
})
} else if (meq > 0) { # equality constraints are present
# alongside the inequalities
# Zhang, Haibo, and Rangaiah, G. P. (2012).
# An efficient constraint handling method with integrated differential
# evolution for numerical and engineering optimization.
# Computers and Chemical Engineering 37, 74-88.
expression({
htrial <- constr1(trial)
TAVtrial <- sum( pmax(htrial, 0) )
if (TAVtrial > mu) {
if (TAVtrial <= TAVpop[i]) { # trial and target are both
pop[, i] <- trial # infeasible, the one with smaller
hpop[, i] <- htrial # constraint violation is chosen
F[, i] <- Ftrial # or trial vector when both are
CR[i] <- CRtrial # solutions of equal quality
pF[i] <- pFtrial
TAVpop[i] <- TAVtrial
}
} else if (TAVpop[i] > mu) { # trial is feasible and target is not
pop[, i] <- trial
fpop[i] <- fn1(trial)
hpop[, i] <- htrial
F[, i] <- Ftrial
CR[i] <- CRtrial
pF[i] <- pFtrial
TAVpop[i] <- TAVtrial
} else { # between two feasible solutions, the
ftrial <- fn1(trial) # one with better objective function
if (ftrial <= fpop[i]) { # value is chosen
pop[, i] <- trial # or trial vector when both are
fpop[i] <- ftrial # solutions of equal quality
hpop[, i] <- htrial
F[, i] <- Ftrial
CR[i] <- CRtrial
pF[i] <- pFtrial
TAVpop[i] <- TAVtrial
FF <- sum(TAVpop <= mu)/NP
mu <- mu*(1 - FF/NP)
}
}
})
} else { # only inequality constraints are present
expression({
htrial <- constr1(trial)
TAVtrial <- sum( pmax(htrial, 0) )
if (TAVtrial > mu) {
if (TAVtrial <= TAVpop[i]) { # trial and target both infeasible
pop[, i] <- trial
hpop[, i] <- htrial
F[, i] <- Ftrial
CR[i] <- CRtrial
pF[i] <- pFtrial
TAVpop[i] <- TAVtrial
}
} else if (TAVpop[i] > mu) { # trial is feasible and target is not
pop[, i] <- trial
fpop[i] <- fn1(trial)
hpop[, i] <- htrial
F[, i] <- Ftrial
CR[i] <- CRtrial
pF[i] <- pFtrial
TAVpop[i] <- TAVtrial
FF <- sum(TAVpop <= mu)/NP
mu <- mu*(1 - FF/NP)
} else { # two feasible solutions
ftrial <- fn1(trial)
if (ftrial <= fpop[i]) {
pop[, i] <- trial
fpop[i] <- ftrial
hpop[, i] <- htrial
F[, i] <- Ftrial
CR[i] <- CRtrial
pF[i] <- pFtrial
TAVpop[i] <- TAVtrial
FF <- sum(TAVpop <= mu)/NP
mu <- mu*(1 - FF/NP)
}
}
})
}
fn1 <- function(par) fn(par, ...)
if (!is.null(constr))
constr1 <-
if (meq > 0) {
eqI <- 1:meq
function(par) {
h <- constr(par, ...)
h[eqI] <- abs(h[eqI]) - eps
h
}
} else function(par) constr(par, ...)
use.jitter <- !is.null(jitter_factor)
# Zielinski, Karin, and Laur, Rainer (2008).
# Stopping criteria for differential evolution in
# constrained single-objective optimization.
# In: U. K. Chakraborty (Ed.), Advances in Differential Evolution,
# SCI 143, Springer-Verlag, pp 111-138
conv <- expression(
( do.call(compare_to, list(fpop)) - fpop[x.best.ind] )/fnscale
)
# Initialization
d <- length(lower)
pop <- matrix(runif(NP*d, lower, upper), nrow = d)
if (!is.null(add_to_init_pop)) {
pop <- unname(cbind(pop, add_to_init_pop))
NP <- ncol(pop)
}
stopifnot(NP >= 4)
# Combine jitter with dither
# Storn, Rainer (2008).
# Differential evolution research - trends and open questions.
# In: U. K. Chakraborty (Ed.), Advances in Differential Evolution,
# SCI 143, Springer-Verlag, pp 11-12
F <- if (use.jitter)
(1 + jitter_factor*runif(d, -0.5, 0.5)) %o% runif(NP, Fl, Fu)
else matrix(runif(NP, Fl, Fu), nrow = 1)
CR <- runif(NP)
pF <- runif(NP)
fpop <- apply(pop, 2, fn1)
stopifnot(is.vector(fpop), !anyNA(fpop), !is.nan(fpop), !is.logical(fpop))
if (!is.null(constr)) {
hpop <- apply(pop, 2, constr1)
stopifnot(is.matrix(hpop) || is.vector(hpop),
!anyNA(hpop), !is.nan(hpop), !is.logical(hpop))
if (is.vector(hpop)) dim(hpop) <- c(1, length(hpop))
TAVpop <- apply( hpop, 2, function(x) sum(pmax(x, 0)) )
mu <- median(TAVpop)
}
popIndex <- 1:NP
x.best.ind <- which.best(fpop)
converge <- eval(conv)
rule <- if (!is.null(constr))
expression(converge >= tol || any(hpop[, x.best.ind] > 0))
else expression(converge >= tol)
convergence <- 0
iteration <- 0
while (eval(rule)) { # Generation loop
if (iteration >= maxiter) {
warning("maximum number of iterations reached without convergence")
convergence <- 1
break
}
iteration <- iteration + 1
for (i in popIndex) { # Start loop through population
# Equalize the mean lifetime of all vectors
# Price, KV, Storn, RM, and Lampinen, JA (2005)
# Differential Evolution: A Practical Approach to
# Global Optimization. Springer, p 284
i <- ((iteration + i) %% NP) + 1
# Self-adjusting parameter control scheme
Ftrial <- if (runif(1) <= tau_F) {
# Combine jitter with dither
if (use.jitter)
runif(1, Fl, Fu) * (1 + jitter_factor*runif(d, -0.5, 0.5))
else runif(1, Fl, Fu)
} else F[, i]
CRtrial <- if (runif(1) <= tau_CR) runif(1) else CR[i]
pFtrial <- if (runif(1) <= tau_pF) runif(1) else pF[i]
# DE/rand/1/either-or/bin
X.i <- pop[, i]
# Randomly pick 3 vectors all diferent from target vector
r <- sample(popIndex[-i], 3)
X.base <- pop[, r[1L]]
X.r1 <- pop[, r[2L]]
X.r2 <- pop[, r[3L]]
trial <- handle.bounds(performReproduction(), X.base)
eval(child)
x.best.ind <- which.best(fpop)
}
converge <- eval(conv)
if (trace && (iteration %% triter == 0))
cat(iteration, ":", "<", converge, ">", "(", fpop[x.best.ind], ")",
pop[, x.best.ind],
if (!is.null(constr))
paste("{", which(hpop[, x.best.ind] > 0), "}"),
fill = TRUE)
}
res <- list(par = pop[, x.best.ind],
value = fpop[x.best.ind],
iter = iteration,
convergence = convergence)
if (details) {
res$poppar <- pop
res$popcost <- fpop
}
res
}
## Not exported, and only used because CRAN checks must be faster
doExtras <- function() {
interactive() || nzchar(Sys.getenv("R_DEoptimR_check_extra")) ||
identical("true", unname(Sys.getenv("R_PKG_CHECKING_doExtras")))
}
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