hho's Introduction

Harris Hawks Optimization (HHO)

This repository contains the Harris Hawks Optimization code (matlab M-file) for optimizing the benchmark function:

.
├── img
|   ├── convergence-2d.png
|   ├── convergence-50d.png
|   ├── logo.jpg 
|   ├── schwefel_222.gif
|   └── trajectory.png
├── _config.yml
├── hho.m —— [function of harris hawks optimization]
├── LICENSE
├── README.md
└── test.m —— [a test for 'hho.m']

Usage

[fbst, xbst, performance] = hho ( objective, d, lmt, n, T, S )

where

objective is the function handle of the objective function
d is the dimension of the design variable
lmt is the limit of the design variable; is a matrix with d rows and 2 column, and the first column contains lower limits of all dimension while the second upper ones.
n is the population size of the particle swarm
T is the maximum iteration times.
fbst is the fitness of the optimal solution
xbst is the optimal solution
performance is contains the best fitness value, the average fitness value and the standard deviation.

Test

For instance, a benchmark Schwefel 2.22 is chosen for a test:

Here, this benchmark is coded as a function handle

schwefel_222 = @(x) sum(abs(x))+prod(abs(x));

where x is a d-by-n matrix.

To run the test by executing

test.m

The test including two runs:

30-d benchmark

The results includes an optimal solution, its fitness and an execution time. Also, a plot of the convergence curve of fitness is shown.
2-d benchmark

The results includes an optimal solution, its fitness and an execution time. Also, a plot of the convergence curve of fitness, a plot of the trajectory of global optimal are shown.