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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering
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Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering

机译：改进的CAT群优化算法解决全局优化问题及其在群集中的应用

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摘要

This paper presents a cat swarm optimization (CSO) algorithm for solving global optimization problems. In CSO algorithm, some modifications are incorporated to improve its performance and balance between global and local search. In tracing mode of the CSO algorithm, a new search equation is proposed to guide the search toward a global optimal solution. A local search method is incorporated to improve the quality of solution and overcome the local optima problem. The proposed algorithm is named as Improved CSO (ICSO) and the performance of the ICSO algorithm is tested on twelve benchmark test functions. These test functions are widely used to evaluate the performance of new optimization algorithms. The experimental results confirm that the proposed algorithm gives better results than the other algorithms. In addition, the proposed ICSO algorithm is also applied for solving the clustering problems. The performance of the ICSO algorithm is evaluated on five datasets taken from the UCI repository. The simulation results show that ICSO-based clustering algorithm gives better performance than other existing clustering algorithms.
机译：本文介绍了一种用于解决全球优化问题的CAT群优化（CSO）算法。在CSO算法中，结合了一些修改，以改善其在全局和本地搜索之间的性能和平衡。在CSO算法的跟踪模式下，建议新的搜索方程来指导搜索到全局最佳解决方案。纳入了本地搜索方法，以提高解决方案的质量并克服本地最佳问题。该算法被命名为改进的CSO（ICSO），并在12个基准测试功能上测试了ICSO算法的性能。这些测试功能广泛用于评估新优化算法的性能。实验结果证实，所提出的算法比其他算法提供更好的结果。此外，所提出的ICSO算法也应用于解决聚类问题。 ICSO算法的性能在从UCI存储库中拍摄的五个数据集上进行评估。仿真结果表明，基于ICSO的聚类算法比其他现有聚类算法具有更好的性能。

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