Skip to main content
Log in

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The design by multi-objectives optimization implies the optimization of several contradictory objectives simultaneously. In fact there is no optimal solution for one particular objective if the other objectives are considered, but the aim is to simultaneously minimize all the objectives in order to reach an optimal compromise. Optimum is reached if any improvement of one objective induces the degradation of one other. Such an optimum is located on a front called Pareto front. The Pareto front, a set of optimal solutions that are not equivalent, allows us to choose an optimal solution with criteria external to optimization process (economic or functional). In this study, a multi-objective particle swarm optimization (a metaheuristic) algorithm has been used to optimize a wood plastic composite for decking application. This metaheuristic, based on evolutionary techniques, applies to a great diversity of functions objectives: continuous or discrete equations, qualitative knowledge rules and algorithms. The design variables are mainly variables of raw materials production, and the incorporation of a biopolymer, the control of timber particle sizes and chemical or thermal timber changes. The objective functions are equations and an algorithm integrating discrete data in the modelling of creep behavior, water resistance and fossil resources depletion.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Alvarez-Benitez, J.E., Everson, R.M., Fieldsend J.E.: A MOPSO algorithm based exclusively on pareto dominance concepts. Evolutionary multi-criterion optimization, Guanajuato, Mexico. Lecture Notes in Computer Science (Issue), pp. 459–473. Springer (2005)

  2. Bavelas A.: Communication patterns in task-oriented groups. J. Acoust. Soc. Am. 22, 271–282 (1950)

    Article  Google Scholar 

  3. Castéra P., Ndiaye A., Fernandez C., Michaud F.: L’optimisation par essaim particulaire appliquée à la conception de composites à renforts lignocellulosiques. Revue des composites et matériaux avancés 18(2), 185–190 (2008)

    Article  Google Scholar 

  4. Hu, X., Eberhart R., Shi, Y.: Particle swarm with extended memory for multiobjective optimization. IEEE Swarm Intelligence Symposium 2003. Indianapolis, IN, USA. (issue), pp. 193–197. IEEE (2003)

  5. Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle performance. In: Proceedings of the IEEE Congress on Evolutionary Computation. pp. 1931–1938. IEEE, Piscataway (1999)

  6. Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE 530 International Conference on Neural Networks. Part 1 (of 6) 531 4(Issue), pp. 1942–1948. IEEE (1995)

  7. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics. pp. 4104–4109. Piscataway, NI (1997)

  8. Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). pp. 1671–1676. IEEE, Honolulu, HI Piscataway (2002)

  9. Mendes R., Kennedy J., Neves J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(4), 204–210 (2004)

    Article  Google Scholar 

  10. Michaud F., Castera P., Fernandez C., Ndiaye A.: Meta-heuristic methods applied to the design of wood–plastic composites, with some attention to environmental aspects. J. Compos. Mater. 43(5), 533–548 (2009)

    Article  Google Scholar 

  11. Poli R., Kennedy J., Blackwell T.: Particle swarm optimization: an overview. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  12. Reyes-Sierra M., Coello Coello C.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  13. Watts D.J., Strogatz S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amadou Ndiaye.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ndiaye, A., Castéra, P., Fernandez, C. et al. Multi-objective preliminary ecodesign. Int J Interact Des Manuf 3, 237–245 (2009). https://doi.org/10.1007/s12008-009-0080-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-009-0080-x

Keywords

Navigation