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The Scientific World Journal
Volume 2014, Article ID 938239, 16 pages
Research Article

A Hybrid Monkey Search Algorithm for Clustering Analysis

1College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China
2Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis, Nanning Guangxi 530006, China

Received 6 November 2013; Accepted 22 January 2014; Published 4 March 2014

Academic Editors: M. Lopez-Nores, D.-C. Lou, L. Martínez, D. Wu, and L. Xiao

Copyright © 2014 Xin Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Viorica R. Chifu, Ioan Salomie, Emil St. Chifu, Balla Izabella, Cristina Bianca Pop, and Marcel Antal, “Cuckoo search algorithm for clustering food offers,” Proceedings - 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing, ICCP 2014, pp. 17–22, 2014. View at Publisher · View at Google Scholar
  • Mingzhi Ma, Qifang Luo, Yongquan Zhou, Xin Chen, and Liangliang Li, “An Improved Animal Migration Optimization Algorithm for Clustering Analysis,” Discrete Dynamics in Nature and Society, vol. 2015, pp. 1–12, 2015. View at Publisher · View at Google Scholar
  • Yongquan Zhou, Xin Chen, and Guo Zhou, “An improved monkey algorithm for a 0-1 knapsack problem,” Applied Soft Computing, vol. 38, pp. 817–830, 2016. View at Publisher · View at Google Scholar
  • Alireza Chehreghan, and Rahim Ali Abbaspour, “An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm,” Journal of the Indian Society of Remote Sensing, vol. 45, no. 4, pp. 579–590, 2017. View at Publisher · View at Google Scholar
  • Liang Zheng, Zhengbing He, and Tian He, “An anisotropic continuum model and its calibration with an improved monkey algorithm,” Transportmetrica A: Transport Science, vol. 13, no. 6, pp. 519–543, 2017. View at Publisher · View at Google Scholar
  • Haiyun Li, Haifeng Li, Xin Chen, and Kaibin Wei, “An Improved Pigeon-Inspired Optimization for Clustering Analysis Problems,” International Journal of Computational Intelligence and Applications, vol. 16, no. 02, pp. 1750014, 2017. View at Publisher · View at Google Scholar
  • Tanachapong Wangchamhan, Sirapat Chiewchanwattana, and Khamron Sunat, “Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric, categorical, and mixed-type data clustering,” Expert Systems with Applications, vol. 90, pp. 146–167, 2017. View at Publisher · View at Google Scholar
  • R. Vasundhara Devi, S. Siva Sathya, and Nilabh Kumar, “Monkey algorithm for robot path planning and vehicle routing problems,” 2017 International Conference on Information Communication and Embedded Systems, ICICES 2017, 2017. View at Publisher · View at Google Scholar