Hypercube optimization based global solution in numerical benchmark and sensor localization

  • Authors

    • S R.Sujatha
    • M Siddappa
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10073
  • Self adaptive differential evolution, Dynamic weight PSO.

  • Abstract

    An original learning algorithm for solving global numerical optimization problems is proposed. The proposed algorithm is strong stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The hypercube optimization algorithm includes the initialization and evaluation process, and searching space process. The designed HO algorithm is tested on specific benchmark functions. The comparative performance analysis have made against with other approaches of dynamic weight particle swarm optimization and self-adaptive differential evolution algorithm. Convergence characteristics of self-adaptive differential evolution algorithm has deliver the much better functional   value in compare to dynamic weight based particle swarm optimization.

  • References

    1. [1] G. S. Fishman, Monte Carlo: Concepts, Algorithms, and Applications, Springer, New York, NY, USA, 1995.

      [2] G. P. Lepage, “A new algorithm for adaptive multidi-mensional integration,†Journal of Computational Physics, vol. 27, no. 2, pp. 192–203, 1978. View at Publisher • View at Google Scholar • View at Scopus.

      [3] D. J. Weiss and G. G. Kingsbury, “Application of com-puterized adaptive testing to educational problems,†Journal of Educational Measurement, vol. 21, pp. 361–375, 1984. View at Google Scholar.

      [4] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learningâ€, Addison-Wesley Publishing, 1989.

      [5] J. H. Holland, “Adaptation in Natural and Artificial Systems, University of Michigan Pressâ€, Ann Arbor, Mich, USA, 1975. View at MathSciNet.

      [6] K. Deb, A. Anand, and D. Joshi, “A computationally efficient evolutionary algorithm for real-parameter optimization,†Evolutionary Computation, Vol. 10, No. 4, pp. 371–395, 2002. View at Publisher • View at Google Scholar • View at Scopus.

      [7] R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,†Journal of Global Optimization, Vol. 11, no. 4, pp. 341–359, 1997. View at Publisher • View at Google Scholar • View at MathSciNet.

      [8] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,†in Proceedings of the IEEE International Conference on NeuralNetworks, Vol. 4, pp. 1942–1948, IEEE, Perth, Australia, December 1995. View at Publisher • View at Google Scholar • View at Scopus.

      [9] S. Kirkpatrick, J. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,†Science, Vol. 220, No. 4598, pp. 671–680, 1983. View at Publisher • View at Google Scholar • View at MathSciNet.

      [10] F. Glover and M. Laguna, Tabu Search, Kluwer Academic Publishers, Boston, Mass, USA, 1997.

      [11] D. Parabola, “Artificial bee colony algorithm,†Scholar-pedia, Vol. 5, No. 3, article 6915, 2010. View at Publisher • View at Google Scholar.

      [12] R. H. Abiyev and M. Menekay, “Fuzzy portfolio selec-tion using genetic algorithm,†Soft Computing, Vol. 11, No. 12, pp. 1157–1163, 2007. View at Publisher • View at Google Scholar • View at Scopus.

      [13] R. H. Abiyev, “Fuzzy wavelet neural network for pre-diction of electricity consumptionâ€,Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, Vol. 23, No. 2, pp. 109–118, 2009. View at Pub-lisher • View at Google Scholar • View at Scopus .

      [14] T. Vidal, T. G. Crainic, M. Gendreau, N. Lahr chi, and W. Rei, “A hybrid genetic algorithm for multidepot and periodic vehicle routing problems,†Operations Research, Vol. 60, No. 3, pp. 611–624, 2012. View at Publisher • View at Google Scholar • View at MathSciNet • View at Scopus.

      [15] X. Yang, C.-B. Hu, K.-X. Peng, and C.-N. Tong, “Load distribution of evolutionary algorithm for complex-process optimization based on differential evolutionary strategy in hot rolling process,†Mathematical Problems in Engineering, Vol. 2013, Article ID 675381, 8 pages, 2013. View at Publisher • View at Google Scholar • View at Scopus.

      [16] L. Zhang, M. Zhang, W. Yang, and D. Dong, “Golden ratio genetic algorithm based approach for modeling and analysis of the capacity expansion of urban road traffic network,†Computational Intelligence and Neuroscience, Vol. 2015, Article ID 512715, 9 pages, 2015. View at Publisher • View at Google Scholar

      [17] J.-T. Tsai, J.-H. Chou, and W.-H. Ho, “Improved quantum-inspired evolutionary algorithm for engineering design optimization,†Mathematical Problems in Engineering, Vol. 2012, Article ID 836597, 27 pages, 2012. View at Publisher • View at Google Scholar • View at MathSciNet.

      [18] C. Grosan and A. Abraham, “A novel global optimization technique for high dimensional functions,†International Journal of Intelligent Systems, Vol. 24, No. 4, pp. 421–440, 2009. View at Publisher • View at Google Scholar • View at Scopus.

      [19] Z. Yang, K. Tang, and X. Yao, “Differential evolution for high-dimensional function optimization,†in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 3523–3530, IEEE, Singapore, September 2007. View at Publisher • View at Google Scholar • View at Scopus.

      [20] X. You, “Differential evolution with a new mutation operator for solving high dimensional continuous optimization problems,†Journal of Computational Information Systems, Vol. 6, No. 9, pp. 3033–3039, 2010. View at Google Scholar • View at Scopus.

      [21] J. Brest, A. Zamuda, B. BoÅ¡ković, M. S. MauÄec, and V. Žumer, “High-dimensional real-parameter optimization using self-adaptive differential evolution algorithm with population size reduction,†in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '08), pp. 2032–2039, Hong Kong, June 2008. View at Publisher • View at Google Scholar • View at Scopus.

      [22] P. Korosec and J. Silc, “High-dimensional real-parameter optimization using the differential ant-stigmergy algorithm,†International Journal of Intelligent Computing and Cybernetics, Vol. 2, No. 1, pp. 34–51, 2009. View at Publisher • View at Google Scholar • View at MathSciNet • View at Scopus.

      [23] Rastko R. Selmic, Vir V. Phoha, Abdul Serwadda, “Localization and Tracking in WSNsâ€, Wireless Sensor Networks,pp. 155-177,Date: 03 November 2016.

      [24] Sujatha S R and, M.Siddappa, “Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Particle Swarm Optimization and Differential Evolutionâ€, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Vol. 19, Issue 2, Ver. III (Mar.-Apr. 2017), PP 07-12 www.iosrjournals.org.

      Sujatha S R and M.Siddappa, “Self-Adaptive Differential Evolution based Localization of Sensors in Wireless Sensor Network†International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 1 (2018) pp. 86-90 © Research India Publications. http://www.ripublication.com- View at Scopus

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  • How to Cite

    R.Sujatha, S., & Siddappa, M. (2018). Hypercube optimization based global solution in numerical benchmark and sensor localization. International Journal of Engineering & Technology, 7(2.6), 88-92. https://doi.org/10.14419/ijet.v7i2.6.10073

    Received date: 2018-03-11

    Accepted date: 2018-03-11

    Published date: 2018-03-11