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Quantum-Inspired Differential Evolution on Bloch Coordinates of Qubits

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Advances in Computing, Communication and Control (ICAC3 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 125))

Abstract

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. On the other hand, a variation of the original quantum-inspired evolutionary algorithm (QEA), bloch quantum-inspired evolutionary algorithm (BQEA), is a promising concept which very well suitable for handling global optimization problem of low dimensionality. BQEA applies several quantum computing techniques such as qubit representation based on bloch sphere and rotation gate operator, etc. This paper extends the concept of differential operators to the quantum paradigm and proposes the bloch quantum-inspired differential evolution algorithm (BQDE). The performance of BQDE is found to be significantly superior as compared to BQEA on several benchmark functions.

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References

  1. Price, K., Storn, R.: Differential Evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, International Computer Science Institute, Berkley (1995)

    Google Scholar 

  2. Storn, R., Price, K.: Differential Evolution – a simple and efficient Heuristic for global optimization over continuous spaces. Journal Global Optimization 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  3. Teo, J.: Exploring Dynamic Self-adaptive Populations in Differential Evolution. Soft Computing - A Fusion of Foundations, Methodologies and Applications 10(8), 673–686 (2006)

    Google Scholar 

  4. Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self- Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)

    Article  Google Scholar 

  5. Yang, Z., Tang, K., Yao, X.: Self-adaptive Differential Evolution with Neighborhood Search. In: Proc. IEEE Congress on Evolutionary Computation, Hong Kong, pp. 1110–1116 (2008)

    Google Scholar 

  6. Chakraborty, U.K.: Advances in Differential Evolution. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  7. Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proc. 1996 IEEE Int. Conf. Evolutionary Computation, Piscataway, NJ, pp. 61–66 (1996)

    Google Scholar 

  8. Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transaction on Evolutionary Computation 6(6), 580–593 (2002)

    Article  Google Scholar 

  9. Sun, J., Feng, B., Xu, W.B.: Particle swarm optimization with particles having quantum behavior. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 325–331 (2004)

    Google Scholar 

  10. Jiao, B., Gu, X., Xu, G.: An Improved Quantum Differential Algorithm for Stochastic Flow Shop Scheduling Problem. In: Proc. IEEE International Conference on Control and Automation, pp. 1235–1240 (2009)

    Google Scholar 

  11. Draa, A., Meshoul, S., Talbi, H., Batouche, M.: A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem. The International Arab Journal of Information Technology 7(1), 21–27 (2010)

    Google Scholar 

  12. Su, H., Yang, Y., Zhao, L.: Classification rule discovery with DE/QDE algorithm. Expert Systems with Applications 37, 1216–1222 (2010)

    Article  Google Scholar 

  13. Su, H., Yang, Y.: Quantum-inspired differential evolution for binary optimization. In: The 4-th International Conference on Natural Computation, pp. 341–346 (2008)

    Google Scholar 

  14. Li, P., Li, S.: Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 72(1-3), 581–591 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology, Kharagpur, India

    Ankit Pat

  2. Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, India

    Ashish Ranjan Hota & Avneet Singh

Authors
  1. Ankit Pat
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  2. Ashish Ranjan Hota
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  3. Avneet Singh
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Editor information

Editors and Affiliations

  1. Fr. Conceicao Rodrigues College of Engineering Fr. Agnel Ashram, 400050, Bandstand, Bandra (West) Mumbai, India

    Srija Unnikrishnan , Sunil Surve  & Deepak Bhoir ,  & 

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© 2011 Springer-Verlag Berlin Heidelberg

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Pat, A., Hota, A.R., Singh, A. (2011). Quantum-Inspired Differential Evolution on Bloch Coordinates of Qubits. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication and Control. ICAC3 2011. Communications in Computer and Information Science, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18440-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-18440-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18439-0

  • Online ISBN: 978-3-642-18440-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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