论文标题
改组的青蛙跳跃算法的当前研究和应用：评论
Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review
论文作者
论文摘要
洗牌的青蛙跳跃算法（SFLA）是最广泛的算法之一。它是由Eusuff和Lansey于2006年开发的。SFLA是一种基于人群的元疗法算法，将模因的好处与粒子群优化相结合。它已在各个领域使用，尤其是在工程问题上，由于其实施易于实施和有限的变量。无论是通过与其他众所周知的算法的修改还是杂交来实现它们，已经对算法进行了许多改进，以减轻其缺点。本文回顾了该算法上最相关的作品。首先进行了SFLA的概述，其次是该算法的最新修改和杂交。接下来，讨论算法的最新应用。然后，提出了SLFA及其变体的操作框架，以分析其在不同应用程序中的用途。最后，提出了对算法的未来改进。进行此调查的主要动力，向有兴趣从事该算法的增强或应用的研究人员提供有关SFLA的有用信息
Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application
