Nothing Special   »   [go: up one dir, main page]

Skip to main content

Population Dynamics Indicators for Evolutionary Many-Objective Optimization

  • Conference paper
  • First Online:
Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 714))

Abstract

Recent research on multi- and many-objective optimization has led to the development of various state-of-the-art algorithms which produce satisfactory results for various kinds of problems. However, in real life, the underlying objective functions or the characteristic landscape formed by the objectives may not be known beforehand. This makes it difficult for a user to choose the correct optimization algorithm. This paper proposes new indicators which attempt to summarize the population dynamics across iterations. The statistics of the population movement can help in identifying various features of the problem at hand and the capacity of an algorithm to deal with the challenges corresponding to the features. The analysis of population movement can enable further modifications of an existing algorithm according to the optimization problem. The indicators can also help in the development of adaptive optimization algorithms by providing feedback during the search for optimality.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Similar content being viewed by others

References

  1. Yuan, Y., Xu, H., Wang, B., Yao, X.: A new dominance relation-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evolutionary Computation 20(1), 16–37 (2016), http://dx.doi.org/10.1109/TEVC.2015.2420112

  2. Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolutionary Computation 11(6), 712–731 (2007), http://dx.doi.org/10.1109/TEVC.2007.892759

  3. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. Evolutionary Computation, IEEE Transactions on 18(4), 577–601 (2014)

    Article  Google Scholar 

  4. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. Evolutionary Multiobjective Optimization pp. 105–145 (2005)

    Google Scholar 

  5. Huband, S., Hingston, P., Barone, L., While, R.L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evolutionary Computation 10(5), 477–506 (2006), http://dx.doi.org/10.1109/TEVC.2005.861417

    Article  Google Scholar 

  6. Bader, J., Zitzler, E.: Hype: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Computation 19(1), 45–76 (2011), http://dx.doi.org/10.1162/EVCO_a_00009

    Article  Google Scholar 

  7. Zitzler, E., Künzli, S.: Indicator-Based Selection in Multiobjective Search, pp. 832–842. Springer Berlin Heidelberg, Berlin, Heidelberg (2004), http://dx.doi.org/10.1007/978-3-540-30217-9_84

    Google Scholar 

  8. Hernández Gómez, R., Coello Coello, C.A.: Improved metaheuristic based on the r2 indicator for many-objective optimization. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. pp. 679–686. GECCO ’15, ACM, New York, NY, USA (2015), http://doi.acm.org/10.1145/2739480.2754776

  9. Qiu, X., Xu, J.X., Tan, K.C., Abbass, H.A.: Adaptive cross-generation differential evolution operators for multiobjective optimization. IEEE Transactions on Evolutionary Computation 20(2), 232–244 (2016)

    Article  Google Scholar 

  10. Li, K., Fialho, Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 18(1), 114–130 (2014)

    Article  Google Scholar 

  11. He, Z., Yen, G.G.: Visualization and performance metric in many-objective optimization. IEEE Trans. Evolutionary Computation 20(3), 386–402 (2016), http://dx.doi.org/10.1109/TEVC.2015.2472283

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by the project (DST-INRIA/2015-02/BIDEE/0978) of the Indo-French Centre for the Promotion of Advanced Research (IFCPAR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Monalisa Pal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sengupta, R., Pal, M., Saha, S., Bandyopadhyay, S. (2019). Population Dynamics Indicators for Evolutionary Many-Objective Optimization. In: Panigrahi, C., Pujari, A., Misra, S., Pati, B., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-13-0224-4_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-0224-4_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0223-7

  • Online ISBN: 978-981-13-0224-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics