Adaptive differential evolution, b6e6rl algorithm

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Description

This function serches for the global minimum using b6e6rl variant of adaptive differential evolution.

Usage

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b6e6rl(fn_name, a, b, N, my_eps, max_evals, n0, delta)

Arguments

fn_name

Name of function which minimum is to find

a

Vector of lower bounds of the search space (length=dimension of the search space)

b

Vector of upper bounds of the search space (length=dimension of the search space)

N

Size of population

my_eps

Small positive value, the algortihm stops when fmax-fmin < my_eps

max_evals

Maximum count of function evaluations per one dimension of the problem

n0

Input parameter controling the competition of the strategies, usualy n0=2

delta

Input parameter (critical probability), usualy delta=1/60

Value

x_star

Aproximation of the global minimum point found by search (vector of length=d)

fn_star

Functional value at x_star

func_evals

Count of function evaluations

success

Count of succesfull generations of the trial point

nrst

Count of resets, when any probability value is less than delta

cni

Counts of succesful selection of each strategy (vector of length=12)

Author(s)

Marek Spruzina, University of Ostrava

References

Tvrdik, J. Adaptation in Differential Evolution: A Numerical Comparison. APPL SOFT COMPUT. 2009, Vol. 9, pp. 1149-1155.

Tvrdik, J. Self-adaptive Variants of Differential Evolution with Exponential Crossover. Analele Universitatii de Vest, Timisoara.Seria Matematica-Informatica.. 2009, Vol. 47, pp. 151- 168.

Examples

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##Example of the b6e6rl call

fn_name <- ("f_dejong")
a <- c(-30,-30,-30)
b <- c(30,30,30)
N <- 60
max_evals <- 20000
my_eps <- 0.000001
n0 <- 2
delta <- 1/(5*12)

b6e6rl(fn_name, a, b, N, my_eps, max_evals, n0, delta)

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