Skip to main content
SpringerLink
Log in

Quantum-Inspired Evolution Algorithm: Experimental Analysis

  • Conference paper
Adaptive Computing in Design and Manufacture VI

Abstract

Quantum computing mimics behaviour of atoms in processing information. Unfortunately due to restrictive rules of processing imposed by quantum behaviour only few successful algorithms have been developed in quantum computing. Quantum inspired algorithm is a concept, which employs certain elements of quantum computing to use in a wider class of search and optimisation problems. The main parts of a quantum‐inspired algorithm are the qubits (quantum equivalent of bits) and the gates. Qubits hold the information in a superposition of all the states, while the quantum gates evolve the qubit to achieve the desired objective, which is, in optimization the maximum or the minimum. The paper addresses the ability of the Quantum‐Inspired Evolution Algorithm (QIEA) to solve practical engineering problems. QIEA, which is developed by authors, is based on their previous work and it is improved to test a series of unitary gates. A set of experiments were carried out to investigate the performance of QIEA as for speed, accuracy, robustness, simplicity, generality, and innovation. To assess effectiveness of a new algorithm, there are a set of guidelines proposed by [1]. Based on these guidelines, the paper selected three test functions to carry out a benchmark study. The paper also presents a comparative study between QIEA and classical Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) techniques in order to assess the proposed QIEA.

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

Access this chapter

Log in via an institution
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

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.

References

  1. Barr, R., Golden, B. L., Kelly, J. P., Resende, M. G. and Stewart, W. R., (1995) Designing and Reporting on Computational Experiments with Heuristic Methods. Journal of Heuristics. 1. 9–32.

    Article  MATH  Google Scholar 

  2. Feynman, R., (1982) Simulating physics with computers. International Journal of Theoretical Physics. 21. 467–488.

    Article  MathSciNet  Google Scholar 

  3. Alfares, F. and Esat, I. I. (2003). Quantum Algorithms; How Useful for Engineering Problems. in Proc. of the Seventh World Conference on Integrated Design & Process Technology. Austin, Texas, USA. 669–673.

    Google Scholar 

  4. Hogg, T., (1998) Highly Structured Searches with Quantum Computers. Physical Review Letters. 80. 2473–2476.

    Article  Google Scholar 

  5. Hogg, T. and Portnov, D. A., (2000) Quantum Optimization. Information Sciences. 128(3). 181–197.

    Article  MathSciNet  MATH  Google Scholar 

  6. Moore, M. P. and Narayanan, A., (1995) Quantum-Inspired Computing. Department of Computer Science, University of Exeter, Technical Report No. 344; http://www.dcs.exeter.ac.uk.

    Google Scholar 

  7. Han, K.-H. and Kim, J.-H. (2000). Genetic quantum algorithm and its application to combinatorial optimization problem. in Proc. of the 2000 Conference on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2; 1354–1360.

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  9. Zhang, G., Jin, W. and Li, N., (2003) An Improved Quantum Genetic Algorithm and Its Application. LNAI. 2639. 449–452.

    Google Scholar 

  10. Narayanan, A. and Moore, M. (1996). Quantum-inspired genetic algorithms. in Proc. of the 1996 IEEE Conference on Evolutionary Computation (ICEC’ 96). Nayoya University, Japan: IEEE, 61–66.

    Google Scholar 

  11. Rylander, B., Soule, T., Foster, J. and Alves-Fos, J. (2000). Quantum Evolutionary Programming. in Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2000), 373–379.

    Google Scholar 

  12. Li, B. and Zhuang, Z.-Q., (2002) Genetic algorithm based-on the quantum probability representation. LNCS. 2412. 500–505.

    Google Scholar 

  13. Narayanan, A. and Manneer, T., (2000) Quantum artificial neural network architectures and components. Information Sciences. 128(3). 231–255.

    Article  MathSciNet  MATH  Google Scholar 

  14. Grover, L. K. (1998). Framework for fast quantum mechanical algorithms. in Conference Proceedings of the Annual ACM Symposium on Theory of Computing, 53–62.

    Google Scholar 

  15. Hogg, T., (2000) Quantum Search Heuristics. Physical Review A (Atomic, Molecular, and Optical Physics). 61(5). 052311/1–7.

    Article  Google Scholar 

  16. Grover, L. K. (1999). Quantum Mechanical Searching. in Proceedings of the Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2255–2261.

    Google Scholar 

  17. Deutsch, D., Barenco, A. and Ekert, A., (1995) Universality in Quantum computation. Proc. of Royal Society London A. 449 (1937). 669–677.

    Article  MathSciNet  Google Scholar 

  18. Hey, T., (1999) Quantum computing: an introduction. Computing & Control Engineering Journal. 10(3). 105–112.

    Article  Google Scholar 

  19. Michalewicz, Z., (1999) Genetic Algorithms + Data Structures = Evolution Programs. 3rd, revised and extended ed, Berlin: SpringerVerlag.

    Google Scholar 

  20. Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization. in Proceedings of the IEEE International Conference on Neural Networks, 1942–1948.

    Google Scholar 

  21. Kennedy, J., Eberhart, R. C. and Shi, Y., (2001) Swarm Intelligence, San Francisco: Morgan Kaufmann Publishers.

    Google Scholar 

  22. Salman, A., I., A. and Al-Madani, S., (2002) Particle swarm optimization for task assignment problem. Microprocessors and Microsystems. 26(8). 363–371.

    Article  Google Scholar 

  23. Levy, A., Montalvo, A., Gomez, S. and Galderon, A., (1981) Topics in Global Optimization, New York: Springer-Verlag.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Brunel University, Uxbridge, Middlesex, UB10 3PH, UK

    F. Alfares & I. I. Esat

  2. Department of Electronic Engineering Technology, College of Technological Studies, P.O. Box: 42325, Shuwaikh, 7065, State of Kuwait

    M. Alfares

Authors
  1. F. Alfares
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Alfares
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. I. Esat
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. CEMS, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK

    I. C. Parmee PhD, CEng, MASCE, MEI

Rights and permissions

Reprints and permissions

Copyright information

Âİ 2004 Springer-Verlag London

About this paper

Cite this paper

Alfares, F., Alfares, M., Esat, I.I. (2004). Quantum-Inspired Evolution Algorithm: Experimental Analysis. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-338-1_32

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-829-9

  • Online ISBN: 978-0-85729-338-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation