工程问题的一种新K均值灰狼算法
A New K means Grey Wolf Algorithm for Engineering Problems
摘要：
目的：元启发式算法的发展已经增加了研究人员在商业，科学和工程领域广泛使用它们。一种常见的元启发式优化算法被称为灰狼优化（GWO）。该算法是在模仿狼群搜索和攻击灰狼的过程的基础上实现的。本文的主要目的是克服GWO算法陷入局部最优解的问题。设计或方法或途径：在本文中，K-means聚类算法被用来提高性能的原始灰狼优化分为不同的部分人口。该算法被称为K-means聚类灰狼优化（KMGWO）。结果：结果表明，KMGWO的效率优于GWO。为了评估KMGWO的性能，KMGWO应用于解决10个CEC 2019基准测试函数。结果表明，KMGWO比GWO更好。KMGWO还与猫群优化（CSO），鲸鱼优化蝙蝠算法（WOA-BAT）和WOA进行了比较，因此，KMGWO在性能方面达到了第一名。统计结果表明，KMGWO算法取得了更高的显着性值相比，比较算法。此外，KMGWO是用来解决压力容器的设计问题，它优于结果。独创性/价值：结果证明，KMGWO优于GWO。将KMGWO算法与猫群算法、鲸-蝙蝠算法、WOA算法和GWO算法进行了比较，结果表明KMGWO算法在性能上达到了第一名。同时，将KMGWO用于求解一个经典的工程问题，证明了KMGWO的优越性
英文摘要：
Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior
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。
