具有逃脱机制的蚁狮算法及其应用
The Antlion Algorithm with Escape Mechanism and Its Applications
摘要: 本文介绍了一种受蚂蚁逃脱蚁狮陷阱方式启发而设计的具有逃逸机制的蚁狮算法。该算法的特点是在蚂蚁的随机游走中引入了曲线位移，增强了它们逃离陷阱的能力。我们将本算法与其他三种蚁狮算法进行了比较，并进行了大量测试，结果显示新构建的蚁狮算法具有更优越的性能。此外，我们将该算法与K-Means聚类算法的质心选择过程相结合，以提高其聚类性能。进一步，我们将这种新算法应用于玻璃产品的分类，并取得了良好的结果。本研究展示了将自然机制融入算法设计的有效性，并在现实问题中得到了实际的应用。
Abstract: In this paper, we present an antlion algorithm with an escape mechanism inspired by the way ants avoid antlions’ traps. Our algorithm features a curved displacement in the random walk path of ants, which allows them to escape more efficiently. We compared our algorithm with three other antlion algorithms and conducted extensive testing, which showed that our modified antlion algorithm has superior performance. Furthermore, we integrated our algorithm with the centroid selection process of the K-Means clustering algorithm to improve its clustering performance. We applied this new algorithm to the classification of glass products and achieved good results. Our research demonstrates the effectiveness of incorporating natural mechanisms into algorithm design and how it can lead to practical applications in real-world problems.
文章引用：梁晨, 张立溥. 具有逃脱机制的蚁狮算法及其应用[J]. 计算机科学与应用, 2024, 14(5): 206-218. https://doi.org/10.12677/csa.2024.145129

参考文献

[1] Chakraborty, A. and Kar, A.K. (2017) Swarm Intelligence: A Review of Algorithms. In: Patnaik, S., Yang, X.S. and Nakamatsu, K. Eds., Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, Springer, Cham, 475-494. [Google Scholar] [CrossRef] 
[2] Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Boston.
[3] Dorigo, M., Maniezzo, V. and Colorni, A. (1996) Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26, 29-41. [Google Scholar] [CrossRef] [PubMed]
[4] Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of ICNN’95—International Conference on Neural Networks, Perth, WA, Australia, 27 November-01 December 1995, 1942-1948. [Google Scholar] [CrossRef] 
[5] Yazdani, D., NadjaranToosi, A. and Meybodi, M.R. (2010) Fuzzy Adaptive Artificial Fish Swarm Algorithm. AI 2010: Advances in Artificial Intelligence—23rd Australasian Joint Conference, Adelaide, Australia, 7-10 December 2010, 334-343. [Google Scholar] [CrossRef] 
[6] Karaboga, D. (2005) An Idea Based On Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
https://api.semanticscholar.org/CorpusID:267873429
[7] Engelbrecht, A.P. (2006) Fundamentals of Computational Swarm Intelligence. John Wiley & Sons, Hoboken, NJ, United States.
[8] Xu, Y., Li, X. and Zhang, L. (2015) The Particle Swarm Shooting Method for Solving the Bratu’s Problem. Journal of Algorithms & Computational Technology, 3, 291-302. [Google Scholar] [CrossRef] 
[9] Seyedali, M. (2015) The Ant Lion Optimizer. Advances in Engineering Software, 83, 80-98. [Google Scholar] [CrossRef] 
[10] Kilic, H., Yuzgec, U. and Karakuzu, C. (2020) A Novel Improved Antlion Optimizer Algorithm and Its Comparative Performance. Neural Computing and Applications, 32, 3803-3824. [Google Scholar] [CrossRef] 
[11] Emary, E. and Zawbaa, H.M. (2019) Feature Selection via Lèvy Antlion Optimization. Pattern Analysis and Applications, 22, 857-876. [Google Scholar] [CrossRef] 
[12] Seyedali, M. (2016) SCA: A Sine Cosine Algorithm for Solving Optimization Problems. Knowledge-Based Systems, 96, 120-133. [Google Scholar] [CrossRef] 
[13] Abualigah, L. and Diabat, A. (2021) Advances in Sine Cosine Algorithm: A Comprehensive Survey. Artificial Intelligence Review, 54, 2567-2608. [Google Scholar] [CrossRef] 
[14] Chenwen, W., Shasha, W. and Xuetong, C. (2023) Fuzzy Clustering Algorithm Combined with Cauchy Distribution and Ant Lion Algorithm. Computer Engineering and Applications, 59, 91-98. [Google Scholar] [CrossRef] 
[15] Wu, C.W., Wang, S.S. and Cao, X.T. (2020) Preferred Strategy Based Self-adaptive Ant Lion Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 33, 121-132. [Google Scholar] [CrossRef] 
[16] Bock, H.H. (2007) Clustering Methods: A History of K-Means Algorithms. In: Brito, P., Cucumel, G., Bertrand, P. and De Carvalho, F., Eds., Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin, Heidelberg. [Google Scholar] [CrossRef] 
[17] Kamel, N., Ouchen, I. and Baali, K. (2014) A Sampling-PSO-K-Means Algorithm for Document Clustering. In: Pan, J.S., Krömer, P. and Snášel, V., Eds., Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, Springer, Cham, 238. [Google Scholar] [CrossRef]