Abstract
The dragonfly algorithm developed in 2016. It is one of the algorithms
used by the researchers to optimize an extensive series of uses and
applications in various areas. At times, it offers superior performance
compared to the most well-known optimization techniques. However, this
algorithm faces several difficulties when it is utilized to enhance
complex optimization problems. This work addressed the robustness of the
method to solve real-world optimization issues, and its deficiency to
improve complex optimization problems. This review paper shows a
comprehensive investigation of the dragonfly algorithm in the
engineering area. First, an overview of the algorithm is discussed.
Besides, we also examined the modifications of the algorithm. The merged
forms of this algorithm with different techniques and the modifications
that have been done to make the algorithm perform better are addressed.
Additionally, a survey on applications in the engineering area that used
the dragonfly algorithm is offered. The utilized engineering
applications are the applications in the field of mechanical engineering
problems, electrical engineering problems, optimal parameters, economic
load dispatch, and loss reduction. The algorithm is tested and evaluated
against particle swarm optimization algorithm and firefly algorithm. To
evaluate the ability of the dragonfly algorithm and other participated
algorithms a set of traditional benchmarks (TF1-TF23) were utilized.
Moreover, to examine the ability of the algorithm to optimize large
scale optimization problems CEC-C2019 benchmarks were utilized. A
comparison is made between the algorithm and other metaheuristic
techniques to show its ability to enhance various problems. The outcomes
of the algorithm from the works that utilized the dragonfly algorithm
previously and the outcomes of the benchmark test functions proved that
in comparison with participated algorithms (GWO, PSO, and GA), the
dragonfly algorithm owns an excellent performance, especially for small
to intermediate applications. Moreover, the congestion facts of the
technique and some future works are presented. The authors conducted
this research to help other researchers who want to study the algorithm
and utilize it to optimize engineering problems.