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.

  

  

Reference:

[1] Article: Harris hawks optimization: Algorithm and applications

[2] Repo: BenchmarkFcns