Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Recent Development of Metaheuristics for Clustering

  • Conference paper
Mobile, Ubiquitous, and Intelligent Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 274))

  • 2738 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Glover, F.: Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13(5), 533–549 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  2. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)

    Article  Google Scholar 

  3. William, Welch, J.: Algorithmic complexity: Three np-hard problems in computational statistics. Journal of Statistical Computation and Simulation 15(1), 17–25 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  4. Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. WH Freeman and Company, New York (1990)

    Google Scholar 

  5. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  6. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  7. Rai, P., Singh, S.: A survey of clustering techniques. International Journal of Computer Applications 7(12), 156–162 (2010)

    Article  Google Scholar 

  8. Carpineto, C., Osiński, S., Romano, G., Weiss, D.: A survey of web clustering engines. ACM Computing Surveys 41(3), 17:1–17:38 (2009)

    Google Scholar 

  9. Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of Conference on Research and Development in Information Retrieval, New York, NY, USA, pp. 267–273 (2003)

    Google Scholar 

  10. Getz, G., Gal, H., Kela, I., Notterman, D.A., Domany, E.: Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data. Bioinformatics 19(9), 1079–1089 (2003)

    Article  Google Scholar 

  11. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)

    Article  Google Scholar 

  12. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  13. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Transaction on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  14. Bianchi, L., Dorigo, M., Gambardella, L., Gutjahr, W.: A survey on metaheuristics for tochastic combinatorial optimization. Natural Computing 8, 239–287 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Puchinger, J., Raidl, G.R.: Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 41–53. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report, Erciyes University, Engineering Faculty, Computer Engineering (2005)

    Google Scholar 

  17. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  18. de Oliveira, D.R., Parpinelli, R.S., Lopes, H.S.: Bioluminescent Swarm Optimization Algorithm. Evolutionary Algorithms (2011)

    Google Scholar 

  19. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  20. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A Gravitational Search Algorithm. Information Sciences 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  21. Abbass, H.: MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. In: Proceedings of Computation Congress on Evolutionary Computation, vol. 1, pp. 207–214 (2001)

    Google Scholar 

  22. Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  23. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: An algrorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 4661–4667 (2007)

    Google Scholar 

  24. Pan, Q.-K., Tasgetiren, M.F., Suganthan, P., Chua, T.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences 181(12), 2455–2468 (2011)

    Article  MathSciNet  Google Scholar 

  25. Karaboga, D., Ozturk, C.: A novel clustering approach: artificial Bee Colony (ABC) algorithm. Applied Soft Computing 11(1), 652–657 (2011)

    Article  Google Scholar 

  26. Zhang, Y., Wu, L., Wang, S., Huo, Y.: Chaotic artificial bee colony used for cluster analysis. Intelligent Computing and Information Science 134, 205–211 (2011)

    Article  Google Scholar 

  27. Akay, B., Karaboga, D.: Parameter tuning for the artificial bee colony algorithm. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 608–619. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  28. Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real parameter optimization. Information Sciences 192, 120–142 (2012)

    Article  Google Scholar 

  29. Khan, K., Nikov, A., Sahai, A.: A fuzzy bat clustering method for ergonomic screening of office workplaces. In: Dicheva, D., Markov, Z., Stefanova, E. (eds.) Software, Services and Semantic Technologies S3T 2011. AISC, vol. 101, pp. 59–66. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  30. Yang, X.-S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Engineering Computations 29(5), 464–483 (2012)

    Article  Google Scholar 

  31. Yang, X.-S.: Bat algorithm for multi-objective optimization. International Journal of Bio-Inspired Computation 3(5), 4267–4274 (2011)

    Google Scholar 

  32. Damodaram, R., Valarmathi, M.L.: Phishing website detection and optimization using modified bat algorithm. International Journal of Engineering Research and Applications 2(1), 870–876 (2012)

    Google Scholar 

  33. Krishnanand, K., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings of Computation Congress on Swarm Intelligence Symposium, pp. 84–91 (2005)

    Google Scholar 

  34. Krishnanand, K., Ghose, D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence 3, 87–124 (2009)

    Article  Google Scholar 

  35. Yang, X.-S., Deb, S.: Eagle strategy using lévy walk and firefly algorithms for stochastic optimization. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 101–111. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  36. Gandomi, A., Yang, X.-S., Talatahari, S., Alavi, A.: Firey algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation (2012)

    Google Scholar 

  37. Yang, X.-S.: Firey algorithm, lévy ights and global optimization. In: Research and Development in Intelligent Systems, pp. 209–218 (2010)

    Google Scholar 

  38. Giannakouris, G., Vassiliadis, V., Dounias, G.: Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS, vol. 6040, pp. 101–111. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  39. Łukasik, S., Żak, S.: Firefly algorithm for continuous constrained optimization tasks. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 97–106. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  40. Sarafrazi, S., Nezamabadi-pour, H., Saryazdi, S.: Disruption: A new operator in gravitational search algorithm. Scientia Iranica 18(3), 539–548 (2011)

    Article  Google Scholar 

  41. Askari, H., Zahiri, S.-H.: Decision function estimation using intelligent gravitational search algorithm. International Journal of Machine Learning and Cybernetics 3, 163–172 (2012)

    Article  Google Scholar 

  42. Li, C., Zhou, J.: Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm. Energy Conversion and Management 52(1), 374–381 (2011)

    Article  Google Scholar 

  43. Marinakis, Y., Marinaki, M., Matsatsinis, N.F.: A hybrid clustering algorithm based on honey bees mating optimization and greedy randomized adaptive search procedure. In: Maniezzo, V., Battiti, R., Watson, J.-P. (eds.) LION 2007 II. LNCS, vol. 5313, pp. 138–152. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  44. Niknam, T.: Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators. Journal of Zhejiang University - Science A 9, 1753–1764 (2008)

    Article  MATH  Google Scholar 

  45. Chang, H.: Converging marriage in honey-bees optimization and application to stochastic dynamic programming. Journal of Global Optimization 35, 423–441 (2006)

    Article  MATH  Google Scholar 

  46. Marinakis, Y., Marinaki, M., Dounias, G.: Honey bees mating optimization algorithm for the euclidean traveling salesman problem. Information Sciences 181(20), 4684–4698 (2011)

    Article  MathSciNet  Google Scholar 

  47. Geem, Z.W.: Music-Inspired Harmony Search Algorithm: Theory and Applications. Springer (2009)

    Google Scholar 

  48. Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., Alizadeh, Y.: Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. In: Computer Methods in Applied Mechanics and Engineering, vol. 197(3340), pp. 3080–3091 (2008)

    Google Scholar 

  49. Qi Li, H., Li, L.: A novel hybrid particle swarm optimization algorithm combined with harmony search for high dimensional optimization problems. In: International Conference on Intelligent Pervasive Computing, pp. 94–97 (2007)

    Google Scholar 

  50. Wang, C.-M., Huang, Y.-F.: Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications 37(4), 2826–2837 (2010)

    Article  Google Scholar 

  51. Omran, M.G., Mahdavi, M.: Global-best harmony search. Applied Mathematics and Computation 198(2), 643–656 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  52. Jaberipour, M., Khorram, E.: Two improved harmony search algorithms for solving engineering optimization problems. Communications in Nonlinear Science and Numerical Simulation 15(11), 3316–3331 (2010)

    Article  Google Scholar 

  53. Pan, Q.-K., Suganthan, P., Tasgetiren, M.F., Liang, J.: A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation 216(3), 830–848 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  54. Al-Betar, M.A., Khader, A.T., Liao, I.Y.: A harmony search with multi-pitch adjusting rate for the university course timetabling. In: Geem, Z.W. (ed.) Recent Advances In Harmony Search Algorithm. SCI, vol. 270, pp. 147–161. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  55. Abdechiri, M., Faez, K., Bahrami, H.: Neural network learning based on chaotic imperialist competitive algorithm. In: Proceedings of the International Workshop on Intelligent Systems and Applications, pp. 1–5 (2010)

    Google Scholar 

  56. Duan, H., Xu, C., Liu, S., Shao, S.: Template matching using chaotic imperialist competitive algorithm. Pattern Recognition Letters 31(13), 1868–1875 (2010)

    Article  Google Scholar 

  57. Talatahari, S., Azar, B.F., Sheikholeslami, R., Gandomi, A.: Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation 17(3), 1312–1319 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  58. Abdechiri, M., Faez, K., Bahrami, H.: Adaptive imperialist competitive algorithm (AICA). In: Proceedings of the International Conference on Cognitive Informatics, pp. 940–945 (2010)

    Google Scholar 

  59. Zhang, Y., Wang, Y., Peng, C.: Improved imperialist competitive algorithm for constrained optimization. In: Proceedings of the International Forum on Computer Science-Technology and Applications, vol. 1, pp. 204–207 (2009)

    Google Scholar 

  60. UCI-machine learning repository, http://archive.ics.uci.edu/ml/

Download references

Author information

Authors and Affiliations

  1. Department of Applied Informatics and Multimedia, Chia Nan University of Pharmacy & Science, Tainan, Taiwan

    Chun-Wei Tsai

  2. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

    Wei-Cheng Huang & Ming-Chao Chiang

Authors
  1. Chun-Wei Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-Cheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming-Chao Chiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chun-Wei Tsai .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Biomedical Informatics Neuroscience, Ohio State University Center for Biomedical Engineering, Columbus, Ohio, USA

    Hojjat Adeli

  3. Dept of Computer Education, Jeju National University Teachers College, Jeju Special Self-Governing Province, Korea, Republic of (South Korea)

    Namje Park

  4. Ryerson University Dept. Computer Science, Toronto, Ontario, Canada

    Isaac Woungang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tsai, CW., Huang, WC., Chiang, MC. (2014). Recent Development of Metaheuristics for Clustering. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_93

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40675-1_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40674-4

  • Online ISBN: 978-3-642-40675-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation