Abstract
This work presents an improved approach for multi-objective and multi-physics optimization based on the hierarchical optimization approach of the typical MOCO (“Multi-objective Collaborative Optimization”) whose objective is to solve multi-objective multi-physics optimization problem. In this document, we propose a new hierarchical optimization approach named Improved Multi-objective Collaborative Optimization (IMOCO) whose goal is to decompose the optimization problems of the complex systems hierarchically in two levels (system and disciplinary level) according to the disciplines. In other words, according to the different physical (mechanical-electrical-acoustical) involved in the mechanical structures design. The presented approach uses a NSGA-II “Non-dominated Sorting Genetic Algorithm II” as an optimizer, and uses a coordinator between the system optimizer and the disciplinary optimizer, which has the role, is to ensure consistency between the various disciplines of the complex system. For the purposes of validation of the proposed method, we chose two examples: (i) numerical problem and (ii) engineering problem. These examples are solved using the proposed IMOCO method and the previous approaches. The obtained results are compared well with those obtained from the previous approaches: (i) non-hierarchically based AAO optimization approach and (ii) hierarchically based MOCO optimization approach, which show the good performance of our proposed IMOCO method.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Auxiliary Variable
- Coordination Problem
- Mechanical Optimizer
- Coupling Variable
- Collaborative Optimization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Aute, V., Azarm, S.: Genetic Algorithms Based Approach for Multidisciplinary Multiobjective Collaborative Optimization. In: Proceedings of the 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, VA (2006)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Ghanmi, S., Guedri, M., Bouazizi, M.-L., Bouhaddi, N.: Robust Multi-Objective and Multi-Level Optimization of Complex Mechanical Structures. Mechanical Systems and Signal Processing 25(7), 2444–2461 (2011)
Meynier, C., Teston, F., Certon, D.: A Multiscale Model for Array of Capacitive Micromachined Ultrasonic Transducers. The Journal of the Acoustical Society of America 128(5), 2549–2561 (2010)
Tappeta, R.V., Renaud, J.E.: Multiobjective Collaborative Optimization. Journal of Mechanical Design 119(3), 403–411 (1997)
Wu, J., Azarm, S.: Metrics for quality assessment of a multi-objective design optimization solution set. J. Mech. Des. 123(1), 18–25 (2001)
Yaralioglu, G.G., Ergun, A.S., Bayram, B., Haeggstrom, E., Khuri-Yakub, B.T.: Calculation and Measurement of Electrom-echanical Coupling Coefficient of Capacitive Micromachined Ultrasonic Transducers. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 50(4), 449–456 (2003)
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
Chagraoui, H., Ghanmi, S., Guedri, M., Soula, M., Bouhaddi, N. (2015). Multi-objective and Multi-physics Optimization of Fully Coupled Complex Structures. In: Chouchane, M., Fakhfakh, T., Daly, H., Aifaoui, N., Chaari, F. (eds) Design and Modeling of Mechanical Systems - II. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-17527-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-17527-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17526-3
Online ISBN: 978-3-319-17527-0
eBook Packages: EngineeringEngineering (R0)