论文标题
猫群优化算法 - 调查和绩效评估
Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation
论文作者
论文摘要
本文提出了对CAT群优化（CSO）算法的深入调查和性能评估。 CSO是一种强大而强大的基于元启发式群的优化方法，自出现以来就获得了非常积极的反馈。它一直在解决许多优化问题，并且已经引入了许多变体。但是，在这方面，文献缺乏详细的调查或绩效评估。因此，本文试图审查所有这些作品，包括其发展和应用程序，并相应地对其进行分组。此外，对CSO进行了23个经典基准功能和10个现代基准功能的测试（CEC 2019）。然后将结果与三种新颖而强大的优化算法进行比较，即蜻蜓算法（DA），蝴蝶优化算法（BOA）和健身依赖性优化器（FDO）。然后根据弗里德曼测试对这些算法进行排名，结果表明，CSO总体排名第一。最后，采用统计方法进一步确认CSO算法的表现。
This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.
