Abstract
This paper deals with minimizing aircraft electrical system weight. Because of technological advances that are spreading, electrical system of aircraft is more complex to design and requires new way to be conceived in order to reduce its weight. This paper describes how to optimize weight of harnesses thanks to the Adaptive Multi-Agent System approach. This approach is based on agent cooperation which makes global function of system emerge. Communication between agents is the focus of this approach. We will develop this approach and apply it to the weight optimisation problem. The developed software provides results that are either equivalent or better than those of classical approaches. Moreover, this software may be a precious help to engineer in charge of designing harnesses as it enables to make different tests in a quasi-real time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bernon, C., Camps, V., Gleizes, M., Picard, G.: Engineering adaptive multi-agent systems: the ADELFE methodology. In: Henderson-Sellers, B., Giorgini, P. (eds.) Agent-Oriented Methodologies, pp. 172–202. Idea Group Publishing, New york (2005)
Camps, V.: Vers une théorie de l’auto-organisation dans les systèmes multi-agents basée sur la coopération: application à la recherche d’information dans un système d’information répartie. Ph.D. thesis, Université Paul Sabatier, Toulouse, France (1998)
Capera, D., Georgé, J., Gleizes, M., Glize, P.: The amas theory for complex problem solving based on self-organizing cooperative agents. In: TAPOCS Workshop at 12th IEEE WETICE, pp. 383–388 (2003)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Glize, P.: L’Adaptation des Systèmes à Fonctionnalité Emergente par Auto-Organisation Coopérative. Ph.D. thesis (2001)
Glover, F., Laguna, M.: Tabu Search. Kluwer, Norwell (1997)
Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1993)
Jorquera, T.: An adaptive multi-agent system for self-organizing continuous optimization. Ph.D. thesis (2013)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann, Burlington (2001)
Kirkpatrick, S., Gellat, C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Talbi, E.: Metaheuristics: From Design to Implementation. Wiley, Hoboken (2009)
Welcomme, J., Gleizes, M., Redon, R.: Self-organising multi-agent system managing complex system design application to conceptual aircraft design. Int. Trans. Syst. Sci. Appl. Systemics Inform. World Netw. (SIWN), Special issue: Self-organized Networked Syst. 5(3), 208–221 (2009)
Yokoo, M., Durfee, E., Kubawara, K.: The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans. Knowl. Data Eng. 10, 673–685 (1998)
Acknowledgements
This work was realized within the French national project ‘Smart Harness’. This project is co-funded by the ‘Ministère de l’Économie, des Finances et de l’Industrie’ and the ‘Région Midi-Pyrénées’ and labeled by the pole of competitiveness Aerospace Valley. Upetec and Irit are specifically involved in the smart harness optimizer work package, in collaboration with the Labinal/Safran Engineering Services Company.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Combettes, S., Sontheimer, T., Rougemaille, S., Glize, P. (2015). Multi-Agent Cooperation for Optimizing Weight of Electrical Aircraft Harnesses. In: Bulling, N. (eds) Multi-Agent Systems. EUMAS 2014. Lecture Notes in Computer Science(), vol 8953. Springer, Cham. https://doi.org/10.1007/978-3-319-17130-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-17130-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17129-6
Online ISBN: 978-3-319-17130-2
eBook Packages: Computer ScienceComputer Science (R0)