Skip to main content
SpringerLink
Log in

Parallel Improved Quantum Evolutionary Algorithm for Complex Optimization Problems

  • Conference paper
  • First Online:
Smart Computing and Communication (SmartCom 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13202))

Included in the following conference series:

  • 1089 Accesses

Abstract

As for the problems of premature convergence, slow convergence and long computing time in solving complex continuous function optimization by traditional quantum evolutionary algorithm, a dynamic parallel quantum evolutionary algorithm for solving complex continuous function optimization problem is proposed in this paper. Multi population co-evolution is adopted, and each sub-population evolves in different search areas according to their own evolution objectives to form a parallel search mode, which can speed up the algorithm convergence and avoid premature convergence; Quantum computation is introduced into the differential evolution algorithm. In this method, the probability amplitude representation of qubits is applied to the real number coding of chromosomes, the chromosome position is updated by quantum mutation, quantum crossover and quantum selecting operations, the two probability amplitudes of qubits are exchanged by quantum non-gate, and an adaptive operator is introduced to improve the population diversity, It can not only prevent the premature convergence of the algorithm, but also make the algorithm converge faster and improve the problem-solving ability of the optimization algorithm. Taking the function extreme value problem as an example, the effectiveness of the algorithm is verified by this algorithm.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

References

  1. Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proceedings of IEEE International Conference on Evolutionary Computation (2002)

    Google Scholar 

  2. Abbass, H.A.: The self-adaptive Pareto differential evolution algorithm. In: Evolutionary Computation, 2002. CEC 2002. Proceedings of the 2002 Congress on (2002)

    Google Scholar 

  3. Gao, Y., et al.: Performance and power analysis of high-density multi-GPGPU architectures: a preliminary case study. In: IEEE 17th HPCC (2015)

    Google Scholar 

  4. Zhao, H., Chen, M., et al.: A novel pre-cache schema for high performance android system. Future Gener. Comput. Syst. 56, 766–772 (2016)

    Article  Google Scholar 

  5. Guo, Y., et al.: Optimal data allocation for scratch-pad memory on embedded multi-core systems. In: IEEE ICPP Conference, pp. 464–471 (2011)

    Google Scholar 

  6. Qiu, M., Chen, Z., Liu, M.: Low-power low-latency data allocation for hybrid scratch-pad memory. IEEE Embed. Syst. Lett. 6(4), 69–72

    Google Scholar 

  7. Zhang, L., Qiu, M., Tseng, W., Sha, E.: Variable partitioning and scheduling for MPSoC with virtually shared scratch pad memory. J. Signal Process. Syst. 58(2), 247–265 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evolut. Comput. 9(2), 126–142 (2005)

    Article  Google Scholar 

  10. Gropp, W.D., Lusk, E.L., Skjellum, A.: Using MPI–portable parallel programming with the message-parsing interface (1994)

    Google Scholar 

  11. Pacheco, P.S.: Parallel Programming with MPI. Argonne National Laboratory, Lemont (1997)

    Google Scholar 

  12. You, X., Sheng, L., Shuai, D.: You, X., Liu, S., Shuai, D.: On parallel immune quantum evolutionary algorithm based on learning mechanism and its convergence. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) Advances in Natural Computation. ICNC 2006. LNCS, vol. 4221, pp. 908–913. Springer, Berlin, Heidelberg (2006). https://doi.org/10.1007/11881070_119

  13. Mikki, S.M., Kishk, A.A.: Quantum particle swarm optimization for electromagnetics. IEEE Trans. Antennas Propag. 54(10), 2764–2775 (2006)

    Article  Google Scholar 

  14. Nodehi, A., Tayarani, M., Mahmoudi, F.: A novel functional sized population quantum evolutionary algorithm for fractal image compression. In: Computer Conference (2009)

    Google Scholar 

  15. Neto, O., Pacheco, M.: A parallel evolutionary algorithm to search for global minima geometries of heterogeneous ab initio atomic clusters. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5–8 June 2011

    Google Scholar 

  16. Xin, W., Fujimura, S.: Parallel quantum evolutionary algorithms with client-server model for multi-objective optimization on discrete problems. In 2012 IEEE Congress on Evolutionary Computation (2012)

    Google Scholar 

  17. Patvardhan, C., Bansal, S., Srivastav, A.: Parallel improved quantum inspired evolutionary algorithm to solve large size quadratic knapsack problems. Swarm Evol. Comput. 26, 175–190 (2016)

    Article  Google Scholar 

  18. Li, J., Li, W.: A new quantum evolutionary algorithm in 0-1 knapsack problem. In: Peng, Hu., Deng, C., Wu, Z., Liu, Y. (eds.) ISICA 2018. CCIS, vol. 986, pp. 142–151. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6473-0_13

    Chapter  Google Scholar 

  19. Zheng, Y., et al.: A variable-angle-distance quantum evolutionary algorithm for 2D HP model. In: Sun, X., Pan, Z., Bertino, E. (eds.) Cloud Computing and Security. ICCCS 2018. LNCS, vol. 11068. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00021-9_30

  20. Jiang, J., Guan, S., Mu, X.: Dynamic assignment model of terminal distribution task based on improved quantum evolution algorithm. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds.) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2019. AISC, vol. 1117. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2568-1_50

  21. Atayan, A.M.: Solving the diffusion-convection problem using MPI parallel computing technology. J. Phys. Conf. Ser. 1902(1), 012098 (2021)

    Google Scholar 

  22. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)

    Article  Google Scholar 

  23. Dong, W.: Research and application based on differential evolution algorithm. Sci. Technol Eng. (2009)

    Google Scholar 

  24. Liu, H.: Genetic algorithm based on function optimization. In: Software Guide (2009)

    Google Scholar 

  25. Zhang, Z.: Novel improved quantum genetic algorithm. Comput. Eng. 36(6), 181–183 (2010)

    Google Scholar 

Download references

Acknowledgments

This research was supported by general scientific research project of education department of Hunan Province in China (21C0338).

Author information

Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, Hunan, China

    Yapeng Sun

  2. School of Computer Science and Engineer, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Yapeng Sun

Authors
  1. Yapeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yapeng Sun .

Editor information

Editors and Affiliations

  1. Texas A&M University-Commerce, Commerce, TX, USA

    Meikang Qiu

  2. Beijing Institute of Technology, Beijing, Beijing, China

    Keke Gai

  3. Tsinghua University, Beijing, China

    Han Qiu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, Y. (2022). Parallel Improved Quantum Evolutionary Algorithm for Complex Optimization Problems. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-97774-0_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97773-3

  • Online ISBN: 978-3-030-97774-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation