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.
翻译:飞龙算法是2016年开发的。它是研究人员用来优化各个领域广泛一系列使用和应用的算法之一。有时,它提供比最著名的优化技术更好的性能。然而,这种算法在用来加强复杂的优化问题时面临若干困难。这项工作解决了解决现实世界优化问题的方法的稳健性,以及解决复杂优化问题的方法的缺陷,从而改善复杂的优化问题。本审查文件展示了对工程领域飞龙算法的全面调查。首先,对算法的概览进行了讨论。此外,我们还研究了算法的修改。这种算法与最著名的优化技术的合并形式以及为使算法更佳而作的修改得到了处理。此外,对使用飞龙算法的工程领域的应用进行了调查。使用的工程应用是在机械工程问题、电气工程问题、最佳参数、经济负荷派遣和减少损失等领域的应用。根据微粒温优化算法和消防算法进行了测试和评价。评估了飞龙算法和其他参与算法的能力,以及为使算法表现得更好。此外,对使用飞龙算法的系统能力进行了一系列传统基准(TF1-23)的升级能力进行了研究,并运用了A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-C-A-A