DEopt {NMOF}

Optimisation with Differential Evolution

Description

The function implements the standard Differential Evolution algorithm.

Usage

DEopt(OF, algo = list(), ...)

Arguments

OF

The objective function, to be minimised. See Details.

algo

A list with the settings for algorithm. See Details and Examples.

...

Other pieces of data required to evaluate the objective function. See Details and Examples.

Details

The function implements the standard Differential Evolution (no jittering or other features). Differential Evolution (DE) is a population-based optimisation heuristic proposed by Storn and Price (1997). DE evolves several solutions (collected in the ‘population’) over a number of iterations (‘generations’). In a given generation, new solutions are created and evaluated; better solutions replace inferior ones in the population. Finally, the best solution of the population is returned. See the references for more details on the mechanisms.

To allow for constraints, the evaluation works as follows: after a new solution is created, it is (i) repaired, (ii) evaluated through the objective function, (iii) penalised. Step (ii) is done by a call to OF; steps (i) and (iii) by calls to algo$repair and algo$pen. Step (i) and (iii) are optional, so the respective functions default to NULL. A penalty is a positive number added to the ‘clean’ objective function value, so it can also be directly written in the OF. Writing a separate penalty function is often clearer; it can be more efficient if either only the objective function or only the penalty function can be vectorised. (Constraints can also be added without these mechanisms. Solutions that violate constraints can, for instance, be mapped to feasible solutions, but without actually changing them. See Maringer and Oyewumi, 2007, for an example.)

Conceptually, DE consists of two loops: one loop across the generations and, in any given generation, one loop across the solutions. DEopt indeed uses, as the default, two loops. But it does not matter in what order the solutions are evaluated (or repaired or penalised), so the second loop can be vectorised. This is controlled by the variables algo$loopOF, algo$loopRepair and algo$loopPen, which all default to TRUE. Examples are given in the vignettes and in the book. The respective algo$loopFun must then be set to FALSE.

All objects that are passed through ... will be passed to the objective function, to the repair function and to the penalty function.

The list algo collects the the settings for the algorithm. Strictly necessary are only min and max (to initialise the population). Here are all possible arguments:

CR

probability for crossover. Defaults to 0.9. Using default settings may not be a good idea.

F

The step size. Typically a numeric vector of length one; default is 0.5. Using default settings may not be a good idea. (F can also be a vector with different values for each decision variable.)

nP

population size. Defaults to 50. Using default settings may not be a good idea.

nG

number of generations. Defaults to 300. Using default settings may not be a good idea.

min, max

vectors of minimum and maximum parameter values. The vectors min and max are used to determine the dimension of the problem and to randomly initialise the population. Per default, they are no constraints: a solution may well be outside these limits. Only if algo$minmaxConstr is TRUE will the algorithm repair solutions outside the min and max range.

minmaxConstr

if TRUE, algo$min and algo$max are considered constraints. Default is FALSE.

pen

a penalty function. Default is NULL (no penalty).

initP

optional: the initial population. A matrix of size length(algo$min) times algo$nP, or a function that creates such a matrix. If a function, it should take no arguments.

repair

a repair function. Default is NULL (no repairing).

loopOF

logical. Should the OF be evaluated through a loop? Defaults to TRUE.

loopPen

logical. Should the penalty function (if specified) be evaluated through a loop? Defaults to TRUE.

loopRepair

logical. Should the repair function (if specified) be evaluated through a loop? Defaults to TRUE.

printDetail

If TRUE (the default), information is printed. If an integer i greater then one, information is printed at very ith generation.

printBar

If TRUE (the default), a txtProgressBar is printed.

storeF

if TRUE (the default), the objective function values for every solution in every generation are stored and returned as matrix Fmat.

storeSolutions

default is FALSE. If TRUE, the solutions (ie, decision variables) in every generation are stored and returned as a list P in list xlist (see Value section below). To check, for instance, the solutions at the end of the ith generation, retrieve xlist[[c(1L, i)]]. This will be a matrix of size length(algo$min) times algo$nP. (To be consistent with other functions, xlist is itself a list. In the case of DEopt, it contains just one element.)

classify

Logical; default is FALSE. If TRUE, the result will have a class attribute TAopt attached. This feature is experimental: the supported methods may change without warning.

drop

If FALSE (the default), the dimension is not dropped from a single solution when it is passed to a function. (That is, the function will receive a single-column matrix.)

Value

A list:

xbest

the solution (the best member of the population), which is a numeric vector

OFvalue

objective function value of best solution

popF

a vector. The objective function values in the final population.

Fmat

if algo$storeF is TRUE, a matrix of size algo$nG times algo$nP containing the objective function values of all solutions over the generations; else NA.

xlist

if algo$storeSolutions is TRUE, a list that contains a list P of matrices and a matrix initP (the initial solution); else NA.

initial.state

the value of .Random.seed when the function was called.

Author(s)

Enrico Schumann

References

Gilli, M., Maringer, D. and Schumann, E. (2019) Numerical Methods and Optimization in Finance. 2nd edition. Elsevier. doi:10.1016/C2017-0-01621-X

Maringer, D. and Oyewumi, O. (2007). Index Tracking with Constrained Portfolios. Intelligent Systems in Accounting, Finance and Management, 15(1), pp. 57–71.

Schumann, E. (2012) Remarks on 'A comparison of some heuristic optimization methods'. http://enricoschumann.net/R/remarks.htm

Schumann, E. (2023) Financial Optimisation with R (NMOF Manual). http://enricoschumann.net/NMOF.htm#NMOFmanual

Storn, R., and Price, K. (1997) Differential Evolution – a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), pp. 341–359.

See Also

GAopt, PSopt

Examples

## Example 1: Trefethen's 100-digit challenge (problem 4)
## http://people.maths.ox.ac.uk/trefethen/hundred.html

OF <- tfTrefethen               ### see ?testFunctions
algo <- list(nP = 50L,          ### population size
             nG = 300L,         ### number of generations
              F = 0.6,          ### step size
             CR = 0.9,          ### prob of crossover
            min = c(-10, -10),  ### range for initial population
            max = c( 10,  10))

sol <- DEopt(OF = OF, algo = algo)
## correct answer: -3.30686864747523
format(sol$OFvalue, digits = 12)
## check convergence of population
sd(sol$popF)
ts.plot(sol$Fmat, xlab = "generations", ylab = "OF")


## Example 2: vectorising the evaluation of the population
OF <- tfRosenbrock     ### see ?testFunctions
size <- 3L             ### define dimension
x <- rep.int(1, size)  ### the known solution ...
OF(x)                  ### ... should give zero

algo <- list(printBar = FALSE,
                   nP = 30L,
                   nG = 300L,
                    F = 0.6,
                   CR = 0.9,
                  min = rep(-100, size),
                  max = rep( 100, size))

## run DEopt
(t1 <- system.time(sol <- DEopt(OF = OF, algo = algo)))
sol$xbest
sol$OFvalue  ### should be zero (with luck)

## a vectorised Rosenbrock function: works only with a *matrix* x
OF2 <- function(x) {
    n <- dim(x)[1L]
    xi <- x[seq_len(n - 1L), ]
    colSums(100 * (x[2L:n, ] - xi * xi)^2 + (1 - xi)^2)
}

## random solutions (every column of 'x' is one solution)
x <- matrix(rnorm(size * algo$nP), size, algo$nP)
all.equal(OF2(x)[1:3],
          c(OF(x[ ,1L]), OF(x[ ,2L]), OF(x[ ,3L])))

## run DEopt and compare computing time
algo$loopOF <- FALSE
(t2 <- system.time(sol2 <- DEopt(OF = OF2, algo = algo)))
sol2$xbest
sol2$OFvalue       ### should be zero (with luck)
t1[[3L]]/t2[[3L]]  ### speedup