Review
Dragonfly Algorithm and Its Hybrids: A Survey on
Performance, Objectives and Applications
Bibi Aamirah Shafaa Emambocus
1,†
, Muhammed Basheer Jasser
1,
*
,†
, Aida Mustapha
2
and Angela Amphawan
1
Citation: Emambocus, B.A.S.; Jasser,
M.B.; Mustapha, A.; Amphawan, A.
Dragonfly Algorithm and Its Hybrids:
A Survey on Performance, Objectives
and Applications. Sensors 2021, 21,
7542. https://doi.org/10.3390/
s21227542
Academic Editors: YangQuan Chen,
Nunzio Cennamo, M. Jamal Deen,
Simone Morais, Subhas
Mukhopadhyay and Junseop Lee
Received: 27 August 2021
Accepted: 11 October 2021
Published: 13 November 2021
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4.0/).
1
Department of Computing and Information Systems, School of Engineering and Technology,
Sunway University, Petaling Jaya 47500, Selangor, Malaysia; 17037730@imail.sunway.edu.my (B.A.S.E.);
angelaa@sunway.edu.my (A.A.)
2
Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun
Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia; aidam@uthm.edu.my
* Correspondence: basheerj@sunway.edu.my
† These authors contributed equally to this work.
Abstract:
Swarm intelligence is a discipline which makes use of a number of agents for solving
optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm
(DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static
swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison
to other swarm intelligence and evolutionary algorithms in numerous applications. There are only
a few surveys about the dragonfly algorithm, and we have found that they are limited in certain
aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in
various domains, and its performance as compared to other swarm intelligence algorithms. We also
analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance
as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA
according to the type of problem that they have been applied to, their objectives, and the methods
that they utilize.
Keywords: dragonfly algorithm; swarm intelligence; optimization
1. Introduction
Optimization algorithms are essential for numerous optimization applications where
usually certain parameters are minimized or maximized by considering an objective function.
Optimization algorithms can be classified as either deterministic or non-deterministic [
1
].
Deterministic algorithms are exact methods, and usually they need a substantial amount of
time and resources for solving large optimization problems. Hence, non-deterministic algo-
rithms, also called heuristic algorithms, are being increasingly used and developed. They
can be based on various natural processes; for example, trajectory-based, physics-based or
population-based, which can be either nature- or bio-inspired [
1
]. Swarm intelligence algo-
rithms are classified as nature-inspired population-based heuristic optimization algorithms.
Swarm intelligence is a discipline which is utilized for solving optimization problems
by producing low cost, fast and robust solutions. Its technique consists of making use of
a number of agents, thereby forming a population in which individuals interact among
themselves and with their environment, to give rise to a global intelligent behavior. There
exist numerous swarm intelligence algorithms, such as ant colony optimization (ACO), grey
wolf optimization (GWO), firefly algorithm (FA), whale optimization algorithm (WOA),
bee colony optimization (BCO), and particle swarm optimization (PSO).
The Dragonfly Algorithm (DA) is a swarm intelligence algorithm that was proposed
in 2016 [
2
], and it is inspired by the behavior of dragonflies in nature. It has been found
to have a higher performance than some of the most popular evolutionary algorithms,
such as the genetic algorithm (GA), and swarm intelligence algorithms such as particle
Sensors 2021, 21, 7542. https://doi.org/10.3390/s21227542 https://www.mdpi.com/journal/sensors
