Abstract
The dragonfly algorithm was developed in 2016. It is one of the algorithms used by 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.
Highlights
One of the newest areas of research is Computational Intelligence (CI)
The results of the proposed work compared to some other algorithms, it was concluded that the exponential function adaptive steps (EOEDA) had better convergence accuracy, and the speed of convergence was faster
The results proved that the Dragonfly algorithm (DA) had better performance for the mentioned problem, and it was competitive with other methods for decreasing maximum side lobe level (MSL)
Summary
Rashid2 1Applied Computer Department, College of Health and Applied Sciences, Charmo University, Sulaimany, Iraq. 2 School of Science and Engineering, Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq Rashid2 1Applied Computer Department, College of Health and Applied Sciences, Charmo University, Sulaimany, Iraq. 2 School of Science and Engineering, Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq
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