Binary Grey Wolf Optimizer

This repository contains the implementation of the novel binary version of the grey wolf optimization (BGWO1) to select optimal feature subset for classification purposes.

Development toolchains:

The proposed algorithm was developed under Debian Linux 64bit operating system(version 11.6), with the following toolchains:

Tool name	Version	Description
g++	10.2.1	C++ compiler (standard 0x11)
ld	2.35.2	GNU linker
make	4.3	GNU make (builder)

How to compile:

Locate the root source code, then type the folowing:

$ make clean
$ make -j4

If the compilation process proceeds successfully, you will see "bgwo" binary under build directory.

Usage:

Binary grey wolf optimizer for feature selection
./bgwo -file [-population_size] [-iterations] [-split] [-debug]
where:
    -file               [required]    Input csv file.
    -population_size    [optinal ]    Search agents size, integer >= 0, default(8)
    -iterations         [optinal ]    Number of iterations, integer >= 0, defualt(70)
    -split              [optinal ]    Training data percent, float >= 0, defualt(0.5)
    -shuffle            [optinal ]    Shuffle the input csv file , defualt(false)
    -debug              [optinal ]    Show debugging info, defualt(false)

How to Run

$ build/bgwo -file dataset/zoo.dat -population_size 100 -iterations 1000

Unittest:

Locate the root source code, then type the folowing:

$ make unittest
$ ./build/unittest

Note: The unittest is based on Google gtest.

Author

Ahmed Dajani, Email: adajani@iastate.edu

Reference

• Binary Grey Wolf Optimizer: https://www.sciencedirect.com/science/article/pii/S0925231215010504