rBAS 
An R module aimed at implementation of Beetle Antennae Search (BAS) Algorithm and its mutations, for example, Beetle Swarm Antenae Search (BSAS) Algorithm.
Installation
rBAS is currently not on CRAN. You can install rBAS from Github with:
devtools::install_github("jywang2016/rBAS")Examples
Use help() to see the document pages about functions in rBAS.
library(rBAS) #load package
help(BASoptim)
help(BSASoptim)Two typocal test functions are applied to validate the efficacy of BAS/BSAS algorithm
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)
}test<-
BASoptim(fn = mich,
lower = c(-6,0), upper = c(-1,2),
seed = 1, n = 100,trace = F)
test$par
#> [1] -4.964687 1.575415
test$value
#> [1] -1.966817
summary(test$df)
#> Length Class Mode
#> x 174 -none- numeric
#> f 87 -none- numeric
#> xbest 174 -none- numeric
#> fbest 87 -none- numericGoldstein-Price function
gold <- function(x){
x1 <- x[1]
x2 <- x[2]
y1 <- 1 + (x1 + x2 + 1)^2*(19 - 14*x1+3*x1^2 - 14*x2 + 6*x1*x2 + 3*x2^2)
y2 <- 30 + (2*x1 -3*x2)^2*(18 - 32*x1 + 12*x1^2+48*x2-36*x1*x2 + 27*x2^2)
return(y1*y2)
}test<-
BASoptim(fn = gold,
lower = c(-2,-2), upper = c(2,2),
seed = NULL, n = 100,trace = F)
test$par
#> [1] 0.001870855 -0.996496153
test$value
#> [1] 3.004756
summary(test$df)
#> Length Class Mode
#> x 174 -none- numeric
#> f 87 -none- numeric
#> xbest 174 -none- numeric
#> fbest 87 -none- numericPressure Vessel
pressure_Vessel <- list(
obj = function(x){
x1 <- floor(x[1])*0.0625
x2 <- floor(x[2])*0.0625
x3 <- x[3]
x4 <- x[4]
result <- 0.6224*x1*x3*x4 + 1.7781*x2*x3^2 +3.1611*x1^2*x4 + 19.84*x1^2*x3
},
con = function(x){
x1 <- floor(x[1])*0.0625
x2 <- floor(x[2])*0.0625
x3 <- x[3]
x4 <- x[4]
c(
0.0193*x3 - x1,
0.00954*x3 - x2,
750.0*1728.0 - pi*x3^2*x4 - 4/3*pi*x3^3
)
}
)result <- BSASoptim(fn = pressure_Vessel$obj,
k = 10,
lower =c( 1, 1, 10, 10),
upper = c(100, 100, 200, 200),
constr = pressure_Vessel$con,
n = 200,
step = 100,
d1 = 4,
pen = 1e6,
steptol = 1e-6,
n_flag = 2,
seed = 2,trace = FALSE)
result$par
#> [1] 14.077163 7.087842 45.335198 140.284669
result$value
#> [1] 6090.567BSAS algorithm
In order to save space, the BSAS algorithm code is executed with trace as FALSE because of too much trace information. You can set trace to TRUE and observe the trace messages.
test<-
BSASoptim(fn = mich,
lower = c(-6,0), upper = c(-1,2),
seed = 12, n = 100,k=5,
trace = F)
test$par
#> [1] -4.968610 1.557598
test$value
#> [1] -1.960214
summary(test$df)
#> Length Class Mode
#> x 202 -none- numeric
#> f 101 -none- numerictest<-
BSASoptim(fn = gold,
lower = c(-2,-2), upper = c(2,2),
seed = 11, n = 100,k=2,
trace = F)
test$par
#> [1] 0.0113079 -0.9948412
test$value
#> [1] 3.030877
summary(test$df)
#> Length Class Mode
#> x 202 -none- numeric
#> f 101 -none- numericMore algorithms
If you want find more algorithm which is avaliable in rBAS.You can visit the Reference page. And click the function name. Furthermore, you can also get more examples of those algorithm application by this way. For example, the Pressure-Vessel problem solved by BSO algorithm can be found here.
BTW, in rBAS 0.1.8, bBAS which is designed for binary-programming is added.
Shiny interface
After rBAS 0.1.0, shiny interface is added into rBAS. Now, the users only need to define their objective problems in R code. And the rest of paramsters in BASoptim or BSASoptim functions can be adjust in the shiny interface.
For example, Michalewicz function can be imported to shiny with united theme as follows,
run_BAS_App(func = mich)
The theme argument is used to provides Bootstrap themes for shiny. More details can be found in shinythemes. Or you can use help(run_BAS_App) to check which themes you can choose.
To do list
Algorithm:
add implement of BSAS algorithmadd BAS-WPT(without parameter tuning)(Xiangyuan Jiang)add binary BAS algorithm (Yue Ruan)add second-order BAS algorithm (Xiaoxiao Li)add BSO(Beetle Swarm Optimizaiton) (Tiantian Wang)- ...
Application:
- constrained optimization:
multi-bar mechanism optimization- system identification for building (resistance-capacity model)
- assembly path planning
lot-sizing problems(binary BAS)
- tests
- tests for more than 20 benchmark functions
UI interface:
- basic interface
add basic shiny graphical interfaceupload constraints.txt
- automatic report generation
- report generation based on rmarkdown
- report download
You can list your requirements in the issues. Furthermore, if you have a good idea or codes about BAS's mutations, pull requests and discussions are welcome. Contact me by email: [email protected]
Authors
Jiangyu Wang
School of Energy and Power Engineering, Huazhong University of Science and Technology
Shuai Li
personal homepage & Googlescholar
Department of Computing, The Hong Kong Polytechnic University
Xiangyuan Jiang
Department of Computing, The Hong Kong Polytechnic University
Citation
citation(package = 'rBAS')
#>
#> To cite package 'rBAS' in publications use:
#>
#> Jiangyu Wang, Shuai Li and Xiangyuan Jiang (2018). rBAS:
#> Implementation of the BAS algorithm and its mutation. R package
#> version 0.1.8. https://github.com/jywang2016/rBAS
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {rBAS: Implementation of the BAS algorithm and its mutation},
#> author = {Jiangyu Wang and Shuai Li and Xiangyuan Jiang},
#> year = {2018},
#> note = {R package version 0.1.8},
#> url = {https://github.com/jywang2016/rBAS},
#> }References
BAS
BSAS
J. Y. Wang, and H. X. Chen, “BSAS: Beetle Swarm Antennae Search Algorithm for Optimization Problems,” arXiv:1807.10470v1. Chinese Handbook of rBAS, please click here.
BAS-WPT
BSO
License
The project is released under the terms of the GPL-3.0.
Copyright © 2018 Jiangyu Wang