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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
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)
Bavelas A.: Communication patterns in task-oriented groups. J. Acoust. Soc. Am. 22, 271–282 (1950)
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)
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)
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)
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)
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)
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)
Mendes R., Kennedy J., Neves J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(4), 204–210 (2004)
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)
Poli R., Kennedy J., Blackwell T.: Particle swarm optimization: an overview. Swarm Intell. 1(1), 33–57 (2007)
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)
Watts D.J., Strogatz S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Author information
Authors and Affiliations
Corresponding author
Rights 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12008-009-0080-x