迭代算法及其在工程中的应用
A survey on dragonfly algorithm and its applications in engineering
摘要：
该算法于2016年开发。它是研究人员用来优化各种领域中广泛的一系列用途和应用的算法之一。有时，与最知名的优化技术相比，它提供了更优越的性能。然而，这种算法面临着一些困难时，它被用来提高复杂的优化问题。该工作解决了该方法解决现实世界的优化问题的鲁棒性，以及其改善复杂优化问题的不足之处。本文综述了工程领域中的迭代算法的全面研究。首先，概述了该算法进行了讨论。此外，我们还研究了算法的修改。该算法的合并形式与不同的技术和修改，已经做了，使算法执行更好的解决。此外，还介绍了该算法在工程领域的应用情况。所利用的工程应用是在机械工程问题、电气工程问题、最优参数、经济负荷分配和降低损耗领域中的应用。通过粒子群优化算法和萤火虫算法对算法进行了测试和评价。为了评估该算法和其他参与算法的能力，使用了一组传统的基准测试（TF1-TF23）。此外，为了检查算法优化大规模优化问题的能力，使用了CEC-C2019基准测试。算法和其他元启发式技术之间的比较，以显示其能力，以提高各种问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
