A survey on dragonfly algorithm and its applications in engineering

• URL: http://arxiv.org/abs/2002.12126v3
• Date: Sat, 28 Aug 2021 07:54:25 GMT
• Title: A survey on dragonfly algorithm and its applications in engineering
• Authors: Chnoor M. Rahman, Tarik A. Rashid, Abeer Alsadoon, Nebojsa Bacanin, Polla Fattah, Seyedali Mirjalili
• Abstract summary: 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. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems.
• Score: 29.190512851078218
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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