Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Classification of Swarm Intelligence Algorithms In Complex Adaptive System Perspective with Statistical Comparative Test Method

Ketut Bayu Yogha Bintoro* -  Department of Informatics, Universitas Trilogi, Indonesia
Silvester Dian Handy Permana -  Department of Informatics, Universitas Trilogi, Indonesia
Open Access Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer

This research aims to classify which SI algorithms have CAS or non-CAS criteria. The statistical comparative test method with 5 (five) characteristic test parameters was used as the proof approach that produces the classification. Based on the hypothesis that has been tested from 15 (fifteen) algorithms compared in this study, It was obtained that 8 of 15 (53.33%) algorithms has the majority of CAS characteristics, 3 of 15 (20%) algorithms has a minority of characteristics of CAS, and 4 out of 15 (26.66%) algorithms did not have CAS characteristics. The result can be a reference to understanding the characteristics of SI algorithms in the CAS and vice versa.

Keywords
swarm intelligence algorithm; complex adaptive system; statistical comparative test; statistical comparison test

How to cite:

Full Text:

Article Metrics:

Article Info
Submitted: 2017-09-14
Published: 2017-10-31
Section: Articles
Language: ID
Statistics: 350 183
In Volume 5, Issue 4, Year 2017 (October 2017)
  1. I. Fister Jr. S. X. Yang, I. Fister, J. Brest, and D. Fister, "A Brief Review of Nature-Inspired Algorithms for Optimization," ELEKTROTEHNIˇSKI VESTNIK, vol. 80, no. 3, pp. 116–1227, 2013.
  2. K. R. Standish, Concept and Definition of Complexity, in Intelligent Complex Adaptive Systems, IGI Global, Hershey, 2008.
  3. R. Reddy, and S. Kalavathi, "Termite Colony Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem," International Journal of Research in Electronics and Communication Technology, vol. 1, no. 4, pp. 34–42, 2014.
  4. R. Tang, S. Fong, S. X. Yang, and S. Deb, "Wolf search algorithm with ephemeral memory," in 7th International Conference on Digital Information Management (ICDIM 2012), 2012, pp. 165–172. doi: 10.1109/ICDIM.2012.6360147.
  5. Y. Liu, X. Wu, and Y. Shen, "Cat Swarm Optimization Clustering (KSACSOC): A Cat Swarm Optimization Clustering Algorithm", Scientific Research and Essays, vol. 7, no. 49, pp. 4176–4185, 2012.
  6. S. X. Yang, and S. Deb, "Eagle Strategy using Levy Walk and Frefly Algorithms for Stochastic Optimization", in Nature Inspired Cooperative Strategies for Optimization (NICSO2010), 2010. pp. 101–111. Springer Berlin Heidelberg.
  7. S. K. Fateen, and A. Bonilla-petriciolet, "Cuckoo Search and Firefly Algorithm," Studies in Computational Intelligence, vol. 516, pp. 315–330, 2014. doi: 10.1007/978-3-319-02141-6.
  8. S. Das, A. Biswas, S. Dasgupta, and A. Abraham, "Bacterial Foraging Optimization Algorithm : Theoretical Foundations, Analysis, and Applications," Foundations of Computational Intelligence, vol. 3, no. 3, pp. 23–55, 2009. doi: 10.1007/978-3-642-01085-9_2.
  9. K. B. Yogha, M. Cendana, and R. Lipikorn, R. (2017) ‘Non-deterministic Finite State Automata as Termites swarm Agent Model’, in 2017 7th International Workshop on Computer Science and Engineering (WCSE), 2017.
  10. J. Ni, L. Wu, X. Fan, and S. X. Yang, "Bioinspired Intelligent Algorithm and its Applications for Mobile Robot Control: a Survey," Computational Intelligence and Neuroscience, vol. 2016. doi: 10.1155/2016/381090.
  11. R. Xiao, Z. Tao, and T. Chen, "An Analytical Approach to the Similarities between Swarm Intelligence and Artificial Neural Network," Transactions of the Institute of Measurements and Control, vol. 34, no. 6, pp. 736-745, 2011.
  12. S. X. Yang, S. Deb, and S. Fong, "Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications," in the Third International Conference on Networked Digital Technologies (NDT 2011). Networked Digital Technologies, Communications in Computer and Information Science, Jul. 2011, v. 136, p. 53-66.
  13. M. Dorigo, and T. Stutze, Ant Colony Optimization, MIT Press, ISBN 0-262-04219-3, England, 2004.
  14. D. Karaboga, and B. Basturk, "On the Performance of Artificial Bee Colony (ABC) Algorithm," Applied Soft Computing, vol. 8, no. 1, pp. 687–697, 2008. doi: 10.1016/j.asoc.2007.05.007.
  15. S. X. Yang, and X. He, "Bat Algorithm: Literature Review and Applications," International Journal of Bio-Inspired Computation, vol. 5, no. 3, pp. 141–149, 2013. doi: 10.1504/IJBIC.2013.055093.
  16. S. Iordache, "Consultant-Guided Search Algorithms with Local Search for the Traveling Salesman Problem’, in Parallel Problem Solving from Nature XI, 2010, pp. 81–90.
  17. S. X. Yang, "Firefly Algorithms for Multimodal Optimization," in International Symposium on Stochastic Algorithms, Berlin, 2009, pp. 169–178.
  18. M. Neshat, A. Adeli, G. Sepidnam, M. Sargolzaei, and A. N. Toosi, "A Review of Artificial Fish Swarm Optimization Methods and Applications," International Journal on Smart Sensing and Intelligent Systems, vol. 5, no. 1, pp. 107–148, 2012.
  19. K. N. Krishnanand, and D. Ghose, "Glowworm Swarm Optimization for Simultaneous Capture of Multiple Local Optima of Multimodal Functions," Swarm Intelligence, vol. 3, no. 2, pp. 87–124, 2009. doi: 10.1007/s11721-008-0021-5.
  20. H. Chen, Y. Zhu, K. Hu, and X. He, "Hierarchical swarm model: A new approach to optimization," Discrete Dynamics in Nature and Society, vol. 2010, 2010. doi: 10.1155/2010/379649.
  21. C. M. Ituarte-Villarreal, N. Lopez, and J. F. Espiritu, "Using the Monkey Algorithm for Hybrid Power Systems Optimization," Procedia Computer Science, vol. 12, pp. 344–349, 2012. doi: 10.1016/j.procs.2012.09.082.

Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro as publisher of the journal. Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. 

JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTSiskom journal are the sole and exclusive responsibility of their respective authors and advertisers.

The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTSiskom]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :  

Eko Didik Widianto (Editor-in-Chief)
Editorial Office of Jurnal Teknologi dan Sistem Komputer
Department of Computer Engineering, Universitas Diponegoro
Jl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275
Telp./Fax: +62-24-76480609
Email: jtsiskom@ce.undip.ac.id