BASoptim: Implementation of the BAS algorithm for optimization...

In jywang2016/rBAS: Implementation of the BAS algorithm and its mutation

Description

You could find more information about BAS in https://arxiv.org/abs/1710.10724.

Usage

BASoptim(fn, init = NULL, lower = c(-6, 0), upper = c(-1, 2),
  constr = NULL, d0 = 0.001, d1 = 3, eta_d = 0.95, l0 = 0,
  l1 = 0, eta_l = 0.95, step = 0.8, eta_step = 0.95, n = 200,
  seed = NULL, trace = T, steptol = 0.01, pen = 1e+05)

Arguments

fn	objective function; function need to be optimized
init	default = NULL, it will generate randomly; Of course, you can specify it.
lower	lower of parameters to be estimated; Default = c(-6,0) because of the test on Michalewicz function of which thelower is c(-6,0); By the way, you should set one of init or lower parameter at least to make the code know the dimensionality of your problem.
upper	upper of parameters; Default = c(-1,2).
constr	constraint function. For example, you can formulate x<=10 as constr = function(x) return(x - 10).
d0	a constant to gurantee that sensing length of antennae d doesn't equal to zero. More specifically, d^t = η_d * d^{t-1} + d_0 where attenuation coefficient η_d belongs to [0,1]
d1	initial value of antenae length. You can specify it according to your problem scale
eta_d	attenuation coefficient of sensing length of antennae
l0	position jitter factor constant.Default = 0.
l1	initial position jitter factor.Default = 0. x = x - step * dir * sign(fn(left) - fn(right)) + l *random(npars)
eta_l	attenuation coefficient of jitter factor. l^t = η_l * l^{t-1} + l_0
step	initial step-size of beetle
eta_step	attenuation coefficient of step-size. step^t = η_{step} * step^{t-1}
n	iterations times
seed	random seed; default = NULL ; The results of BAS depend on random init value and random directions. Therefore, if you set a random seed, for example,seed = 1, the results will remain the same no matter how many times you repeat your experiments.
trace	default = T; trace the process of BAS iteration.
steptol	default = 0.01; Iteration will stop if step-size in current moment is less than steptol.
pen	penalty conefficient usually predefined as a large enough value, default 1e5

Value

A list including best beetle position (parameters) and corresponding objective function value.

References

X. Y. Jiang, and S. Li, BAS: beetle antennae search algorithm for optimization problems, arXiv:1710.10724v1.

Examples

#======== examples start =======================
# BAS application on Michalewicz function
library(rBAS)
mich <- function(x){
  y1 <- -sin(x[1])*(sin((x[1]^2)/pi))^20
  y2 <- -sin(x[2])*(sin((2*x[2]^2)/pi))^20
  return(y1+y2)
}
BASoptim(fn = mich,
         lower = c(-6,0), upper = c(-1,2),
         seed = 1, n = 100,trace = FALSE)
#======== examples end =======================

jywang2016/rBAS documentation built on May 21, 2019, 1:43 a.m.