DOI: https://doi.org/10.14710/jtsiskom.5.4.2017.166-171

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

Department of Informatics, Universitas Trilogi, Indonesia

Received: 14 Sep 2017; Published: 31 Oct 2017.
Open Access Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract

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.

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

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  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

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