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Recent Development of Metaheuristics for Clustering

  • Chun-Wei Tsai
  • Wei-Cheng Huang
  • Ming-Chao Chiang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)

Abstract

Metaheuristics have been successfully applied to quite a lot of services, systems, and products frequently found in our daily life. Until now, none of the metaheuristics ever proposed are perfect for all the optimization problems; rather, each algorithm has its pros and cons. Although several high-performance metaheuristics exist, there is still plenty of room to improve the final result they produce and the computation time they take. Since 2001, quite a few number of novel metaheuristics have been developed to provide a better way for solving the optimization problems. A brief review for eight of these novel metaheuristics is given in this paper. To evaluate the performance of these algorithms, we apply them to a well-known combinatorial optimization problem, data clustering, and the results are analyzed and discussed.

Keywords

Metaheuristics clustering combinatorial optimization problem 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Chun-Wei Tsai
    • 1
  • Wei-Cheng Huang
    • 2
  • Ming-Chao Chiang
    • 2
  1. 1.Department of Applied Informatics and MultimediaChia Nan University of Pharmacy & ScienceTainanTaiwan
  2. 2.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan

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