Skip to main content

Development of a Multi-Objective Genetic Algorithm for the Design of Offshore Renewable Energy Systems


This paper describes the development of a framework using a genetic algorithm in order to aid in the design of a mooring system for offshore renewable energy devices. This framework couples numerical models of the mooring system and structural response to cost models in order for the genetic algorithm to effectively operate considering multiple objectives. The use of this multi-objective optimization approach allows multiple design objectives such as minimum breaking load and the material cost to be minimized simultaneously using an automated mathematical approach. Through the application of this automated approach, a wider set of designs will be considered allowing the system designers to select a design which appropriately balances the trade-off between the competing objectives. In this work, a set of mooring designs that represent efficient solutions for the stipulated constraints are found and presented. The developed framework will be applicable to other offshore technology subsystems allowing multi-objective optimization and reliability to be considered from the design stage in order to improve the design efficiency and aid the industry in using more systematic design approaches.


  • Mooring system
  • Multi-objective optimization
  • Renewable energy systems
  • Genetic algorithm

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   509.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   649.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   649.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions


  1. Baños, R., Manzano-Agugliaro, F., Montoya, F., Gil, C., Alcayde, A., Gómez, J.: Optimization methods applied to renewable and sustainable energy: a review. Renew. Sustain. Energy Rev. 15(4), 1753–1766 (2011).

    CrossRef  Google Scholar 

  2. Pillai, A.C., Chick, J., Johanning, L., Khorasanchi, M., Barbouchi, S.: Comparison of offshore wind farm layout optimizaiton using a genetic algorithm and a particle swarm optimizer. In: Proceedings of the ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2016), Busan, South Korea, vol. 6, pp. 1–11. ASME (2016)

    Google Scholar 

  3. Robertson, A., Jonkman, J., Masciola, M.: Definition of the semisubmersible floating system for Phase II of OC4, Golden, CO, p. 38, September 2014.

  4. Johanning, L., Smith, G., Wolfram, J.: Mooring design approach for wave energy converters. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 220(4), 159–174 (2006).

    Google Scholar 

  5. Weller, S., Johanning, L., Victor, L., Heath, J., Eddy, J., Jensen, R., Banfield, S.: DTOcean Deliverable 4.5 - Mooring and Foundation Module Framework for DTOcean Tool (2015)

    Google Scholar 

  6. Holland, J.H.: Adaptation In Natural And Artificial Systems [Electronic Resource]: An Introductory Analysis With Applications To Biology, Control, And Artificial Intelligence, 2nd edn. MIT Press, Cambridge (1992)

    Google Scholar 

  7. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  8. Deb, K., Pratap, A.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).

  9. Harris, R., Johanning, L., Wolfram, J.: Mooring systems for wave energy converters: a review of design issues and choices. In: MAREC 2004 (2004).

  10. Shafieefar, M., Rezvani, A.: Mooring optimization of floating platforms using a genetic algorithm. Ocean Eng. 34(10), 1413–1421 (2007)

    CrossRef  Google Scholar 

  11. Carbono, A.J.J., Menezes, I.F.M., Martha, L.F.: Mooring pattern optimization using genetic algorithms. In: Structural and Multidisciplinary Optimization, pp. 1–9, June 2005

    Google Scholar 

  12. Heffernan, D.: An introduction to the Python interface to OrcaFlex. Technical report, Orcina, April 2016

    Google Scholar 

  13. Pitt, E., Saulter, A., Smith, H.: The wave power climate at the Wave Hub site. Technical report, Applied Wave Research Report to SWRDA, November 2006

    Google Scholar 

  14. JRC Ocean: DT Ocean Suite - DTOcean Database 1.0.0 (2016).

Download references


This work is funded by the EPSRC (UK) grant for the SuperGen United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1].

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ajit C. Pillai .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pillai, A.C., Thies, P.R., Johanning, L. (2018). Development of a Multi-Objective Genetic Algorithm for the Design of Offshore Renewable Energy Systems. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67987-7

  • Online ISBN: 978-3-319-67988-4

  • eBook Packages: EngineeringEngineering (R0)