Skip to main content

Optimization of Multi-objective Dynamic Optimization Problems with Front-Based Yin-Yang-Pair Optimization

  • Conference paper
  • First Online:
Smart Innovations in Communication and Computational Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 669))

Abstract

Multi-objective Dynamic Optimization Problems (MDOP) are a set of challenging engineering problems in which one or more of the terms in the problem are dependent on independent variables. In this work, we employ a recently proposed stochastic multi-objective optimization algorithm, Front-based Yin-Yang-Pair Optimization, to solve such problems. The algorithm is applied on three Multi-objective Dynamic Optimization Problems (MDOP) from literature: (i) a batch reactor, (ii) a plug flow reactor and (iii) a fed-batch reactor problem. F-YYPO is able to determine efficient Pareto curves for the MDOP problems and shows competitive performance with literature results.

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

Access this chapter

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. Chen, X., W. Du, and F. Qian, Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization. Chemometrics and Intelligent Laboratory Systems, 2014. 136: p. 85–96.

    Google Scholar 

  2. Babu, B.V. and R. Angira, Modified differential evolution (MDE) for optimization of non-linear chemical processes. Computers & Chemical Engineering, 2006. 30(6–7): p. 989–1002.

    Google Scholar 

  3. Campos, M. and R.A. Krohling, Entropy-based bare bones particle swarm for dynamic constrained optimization. Knowledge-Based Systems, 2016. 97: p. 203–223.

    Google Scholar 

  4. Deb, K., Multi-objective optimization using evolutionary algorithms. Vol. 16. 2001: John Wiley & Sons.

    Google Scholar 

  5. Marler, R.T. and J.S. Arora, Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, 2004. 26(6): p. 369–395.

    Google Scholar 

  6. Reyes-Sierra, M. and C.C. Coello, Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2006. 2(3): p. 287–308.

    Google Scholar 

  7. Deb, K., et al., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002. 6(2): p. 182–197.

    Google Scholar 

  8. Xue, F., A.C. Sanderson, and R.J. Graves. Pareto-based multi-objective differential evolution. in IEEE Congress on Evolutionary Computation. 2003. Canberra, Australia.

    Google Scholar 

  9. Punnathanam, V. and P. Kotecha, Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization. Energy Conversion and Management, 2017. 133: p. 332–348.

    Google Scholar 

  10. Punnathanam, V. and P. Kotecha, Yin-Yang-pair Optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 2016. 54: p. 62–79.

    Google Scholar 

  11. Punnathanam, V., Yin-Yang-Pair Optimization: A novel lightweight optimization algorithm (Unpublished Master’s thesis), 2016, Indian Institute of Technology Guwahati: Guwahati, India.

    Google Scholar 

  12. Punnathanam, V. and P. Kotecha. Front-based Yin-Yang-Pair Optimization and its performance on CEC2009 benchmark problems. in International Conference on Smart Innovations in Communications and Computational Sciences. 2017. Punjab, India.

    Google Scholar 

  13. Logist, F., et al., Multi-objective optimal control of chemical processes using ACADO toolkit. Computers & Chemical Engineering, 2012. 37: p. 191–199.

    Google Scholar 

  14. Herrera, F. and J. Zhang, Optimal control of batch processes using particle swam optimisation with stacked neural network models. Computers & Chemical Engineering, 2009. 33(10): p. 1593–1601.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prakash Kotecha .

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

Punnathanam, V., Kotecha, P. (2019). Optimization of Multi-objective Dynamic Optimization Problems with Front-Based Yin-Yang-Pair Optimization. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-8968-8_32

Download citation

Publish with us

Policies and ethics