Abstract
The optimization of complex industrial systems represents a class of difficult problems, due to their embodiment in the physical world, and whose search spaces are disrupted, non-linear and potentially vast. Their parametrization relies on the combination of many variables, each change generally impacting the whole system. Mathematical approaches are limited by the fact that the models are too coarse or non-existent, and by the imprecision of the measurements and the machining of components of such systems. The action cost of the system calls into question population-based heuristics and swarm intelligence where each individual must be tested. We propose a multi-agent approach allowing a global/black-box modeling of the system in terms of input variables and objectives, as well as an agnostic and continuous adaptive optimization, based on sensor feedbacks from the running system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that in reality, in 3 dimensions, there will be 6 agents for the 6 degrees of freedom.
References
Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. J. Artif. Intell. Res. 61, 623–698 (2018)
Hoang, K.D., Yeoh, W.: Dynamic continuous distributed constraint optimization problems. In: Aydogan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds.) PRIMA 2022: Principles and Practice of Multi-Agent Systems, PRIMA 2022, vol. 13753, pp. 475–491. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21203-1_28
Hoang, K.D., Yeoh, W., Yokoo, M., Rabinovich, Z.: New algorithms for continuous distributed constraint optimization problems. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (2020)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and AI. MIT press, Cambridge (1992)
Hussain, K., Mohd Salleh, M.N., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 52, 2191–2233 (2019)
Jorquera, T., Georgé, J.P., Gleizes, M.P., Régis, C.: A natural formalism and a multiagent algorithm for integrative multidisciplinary design optimization. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology - IAT, Atlanta, USA (2013)
Jourdan, L., Basseur, M., Talbi, E.G.: Hybridizing exact methods and metaheuristics: a taxonomy. Eur. J. Oper. Res. 199(3), 620–629 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks. IEEE (1995)
Perles, A., Crasnier, F., Georgé, J.P.: AMAK - a framework for developing robust and open adaptive multi-agent systems. In: 16th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS). Communications in Computer and Information Science book series (CCIS), Spain (2018)
Sharma, M., Kaur, P.: A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem. Arch. Comput. Methods Eng. 28, 1103–1127 (2021)
Sörensen, K.: Metaheuristics-the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)
Wang, Z., Qin, C., Wan, B., Song, W.W.: A comparative study of common nature-inspired algorithms for continuous function optimization. Entropy 23(7), 874 (2021)
Yang, T., et al.: A survey of distributed optimization. Ann. Rev. Control 47, 278–305 (2019)
Ye, D., Zhang, M., Vasilakos, A.V.: A survey of self-organization mechanisms in multiagent systems. IEEE Trans. Syst. Man Cybern.: Syst. 47(3), 441–461 (2016)
Acknowledgements
This work is financially supported by the Occitanie Region (www.laregion.fr) as part of the READYNOV 2019–2020 research program. Quentin Pouvreau is co-funded by the French National Association for Research and Technology (ANRT) (www.anrt.asso.fr) and by ISP System.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pouvreau, Q., Georgé, JP., Bernon, C., Maignan, S. (2023). Optimization of Complex Systems in Photonics by Multi-agent Robotic Control. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-37616-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37615-3
Online ISBN: 978-3-031-37616-0
eBook Packages: Computer ScienceComputer Science (R0)