Dragonfly Algorithm

The main inspiration of the Dragonfly Algorithm (DA) algorithm originates from static and dynamic swarming behaviours. These two swarming behaviours are very similar to the two main phases of optimization using meta-heuristics: exploration and exploitation. Dragonflies create sub swarms and fly over different areas in a static swarm, which is the main objective of the exploration phase. In the static swarm, however, dragonflies fly in bigger swarms and along one direction, which is favourable

Single-objective optimization

DA has been designed to solve single-objective optimization problems. This algorithm has been implemented in a wide range of programming languages. You can download the source code at the bottom of this page.

Download Matlab code of DA

Multi-objective optimization

If you are interested in solving a multi-objective problem using DA, you have to use this code.

Download MODA Matlab code

Binary optimization

If you are interested in solving a binary or discrete using DA, you have to use this code.

Download BDA Matlab code

Matlab DA toolbox

Binary optimization

A user-friendly interface to run DA algorithm with minimum coding.

Download DA Matlab Toolbox

Hyper Learning Binary Dragonfly Algorithm

HLBDA is an enhanced version of the Binary Dragonfly Algorithm (BDA) in which a hyper learning strategy is used to assist the algorithm to escape the local optima and improve searching behavior. (credits: Dr. Jingwei Too)

Download HLBDA

Dragonfly Algorithm

Single-objective optimization

Multi-objective optimization

Binary optimization

Binary optimization

Matlab DA toolbox

Binary optimization

Hyper Learning Binary Dragonfly Algorithm

Hyper Learning Binary Dragonfly Algorithm

Download DA Source Code

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