Skip to main content
SpringerLink
Log in

QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: A New Simple and Accurate Structure for Global Optimization

  • Conference paper
  • First Online:
Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

Abstract

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a simple but powerful structure for global optimization. Six different evolution schemes derived from this structure will be discussed in this paper. There is a close relationship between our proposed structure and Different Evolution (DE) structure, and DE can be considered as a special case of the proposed QUATRE algorithm. The performance of DE is usually dependent on parameter control and mutation strategy. There are 3 control parameters and several mutation strategies in DE, and this makes it a little complicated. Our proposed QUATRE is simpler than DE algorithm as it has only one control parameter and it is logically powerful from mathematical perspective of view. We also use COCO framework under BBOB benchmarks and CEC Competition benchmarks for the verification of the proposed QUATRE algorithm. Experiment results show that though QUATRE algorithm is simpler than DE algorithm, it is more powerful not only on unimodal optimization but also on multimodal optimization problem.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  2. Meng, Z., Pan, J.-S.: A simple and accurate global optimizer for continuous spaces optimization. In: Sun, H., Yang, C.-Y., Lin, C.-W., Lin, J.-S., Snasel, V., Abraham, A. (eds.) Genetic and Evolutionary Computing. AISC, vol. 329, pp. 121–129. Springer, Switzerland (2015)

    Google Scholar 

  3. Meng, Z., Pan, J.-S., Alelaiwi, A.: A new meta-heuristic ebb-tide-fish-inspired algorithm for traffic navigation. Telecommun. Syst. 62, 403–415 (2016)

    Article  Google Scholar 

  4. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995, vol. 4. IEEE (1995)

    Google Scholar 

  5. Kirkpatrick, S., Vecchi, M.P.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  7. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Yang, X.-S.: Harmony search as a metaheuristic algorithm. In: Geem, Z.W. (ed.) Music-Inspired Harmony Search Algorithm. SCI, vol. 191, pp. 1–14. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Meng, Z., Pan, J.-S.: Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl.-Based Syst. 97, 144–157 (2016)

    Article  MathSciNet  Google Scholar 

  11. Rahnamayan, S., Tizhoosh, H.R., Salama, M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  12. Brest, J., et al.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  13. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by National Natural Science Foundation of China (61273290).

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Innovative Information Industry Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jeng-Shyang Pan & Zhenyu Meng

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  3. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, China

    Huarong Xu

  4. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Xiaoqing Li

Authors
  1. Jeng-Shyang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenyu Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huarong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenyu Meng .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Pan, JS., Meng, Z., Xu, H., Li, X. (2016). QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: A New Simple and Accurate Structure for Global Optimization. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42007-3_57

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation