论文标题
关于蜻蜓算法及其在工程中的应用的调查
A survey on dragonfly algorithm and its applications in engineering
论文作者
论文摘要
蜻蜓算法是在2016年开发的。这是研究人员使用的算法之一,以优化各个领域的一系列用途和应用。有时,与最著名的优化技术相比,它提供了出色的性能。但是,当该算法用于增强复杂的优化问题时,该算法会面临一些困难。这项工作解决了解决现实世界优化问题的方法的鲁棒性及其缺乏,以改善复杂的优化问题。这篇评论论文显示了对工程领域中蜻蜓算法的全面研究。首先,讨论了该算法的概述。此外,我们还检查了该算法的修改。该算法的合并形式与不同的技术以及为使算法表现更好的修改所做的修改。此外,还提供了有关使用蜻蜓算法的工程区域应用程序的调查。使用的工程应用程序是机械工程问题,电气工程问题,最佳参数,经济负载调度和减少损失领域的应用。该算法对粒子群优化算法和萤火虫算法进行了测试和评估。为了评估蜻蜓算法和其他参与算法的能力,一组传统基准(TF1-TF23)的能力已被使用。此外,要检查算法优化大规模优化问题CEC-C2019基准的能力。在算法和其他元启发式技术之间进行了比较,以显示其增强各种问题的能力。
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.