BASoptim2: Implementation of the BAS with momentum (also named as...

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

Description

BAS with momentum introduces 'velocity' to the detecting rules of basic BAS. It is more like the individual version of BSO The paper of this algorithm is not available now. If you have any question, please contact Xiaoxiao Li ([e-mail])

Usage

BASoptim2(fn, init = NULL, lower = c(-6, 0), upper = c(-1, 2),
  constr = NULL, c = 2, l0 = 0, l1 = 0, eta_l = 0.95,
  step0 = 5e-05, step = 0.8, eta_step = 0.95, n = 200,
  seed = NULL, trace = T, steptol = step0/2, pen = 1e+05,
  w0 = 0.7, w1 = 0.2, c0 = 0.6)

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).
c	the ratio of step-size and search distance d. d = \frac{step}{c}
l0	position jitter factor constant.Default = 0.
l1	initial position jitter factor.Default = 0.
eta_l	attenuation coefficient of jitter factor. l^t = η_l * l^{t-1} + l_0
step0	the minimal resolution of step-size
step	initial step-size of beetle. step = η_{step}(step-step0) + step0
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 = step0/2; 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
w0	inertia weight; default = 0.7
w1	weight of the difference between left and right antenae. default = 0.2 v = w_0v-w_1dir(f_{left}-f_{right})
c0	the ratio of maximal speed and initial step. v_{max} = c_0step

Value

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

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)
}
fit<-
  BASoptim2(fn = mich,
            lower = c(-6,0),
            upper = c(-1,2),
            n = 100,
            trace = F,
            c = 0.4,#d = 1.2/0.4 = 3
            step = 1.2,
            seed = 1,
            w0 = 0.4,w1 = 0.2, c0 = 0.6)
fit$par;fit$value
func1 <- function(x){
  sum(x^2)
}
fit<-
  BASoptim2(fn = func1,
            lower = c(-100,-100),
            upper = c(100,100),
            n = 100,
            trace = F,
            c = 20,
            step = 100,
            seed = 1,
            w0 = 0.5,w1 = 0.2, c0 = 0.6)
fit$par;fit$value
func2 <- function(x){
  sum((abs(x)-5)^2)
}
fit<-
  BASoptim2(fn = func2,
            lower = c(-10,-10),
            upper = c(10,10),
            n = 100,
            trace = F,
            c = 5,
            step = 5,
            seed = 1,
            w0 = 0.2,w1 = 0.2, c0 = 0.6)
fit$par;fit$value
#======== examples end =======================

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