混合蛙跳算法的研究与应用现状
Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review
摘要：
混合蛙跳算法是应用最广泛的算法之一。它由Eusuff和Lansey于2006年开发。SFLA是一种基于种群的元启发式算法，它结合了模因学和粒子群优化的优点。它已被用于各个领域，特别是在工程问题中，由于其易于实现和有限的变量。已经对该算法进行了许多改进以减轻其缺点，无论是通过修改还是与其他知名算法的混合来实现。本文回顾了该算法的最相关的工作。SFLA的概述首先进行，其次是算法的最新修改和杂交。接下来，最近的应用程序的算法进行了讨论。然后，SLFA及其变体的操作框架，提出了分析他们的使用在不同的应用程序群。最后，对该算法的未来改进提出了建议。进行这项调查的主要动机是为有兴趣从事算法增强或应用的研究人员提供有关SFLA的有用信息
英文摘要：
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
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。
