Abstract
In this paper, a new multiobjective genetic algorithm is employed to support the design of a hydraulic actuation system. First, the proposed method is tested using benchmarks problems gathered from the literature. The method performs well and it is capable of identifying multiple Pareto frontiers in multimodal function spaces. Secondly, the method is applied to a mixed variable design problem where a hydraulic actuation system is analyzed using simulation models. The design problem constitutes of a mixture of determining continuous variables as well as selecting components from catalogs. The multi-objective optimization results in a discrete Pareto front, which illustrate the trade-off between system cost and system performance.
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
Preview
Unable to display preview. Download preview PDF.
References
Andersson J. and Wallace D., 2000, “Pareto optimization using the struggle genetic crowding algorithm,” Submitted for review to ASME Journal of Mechanical Design.
Andersson J., Krus P. and Wallace D., 2000 “Multi-objective optimization of hydraulic actuation systems”, Proceedings of the ASME Design Automation Conferences, DETC2000/DAC-14512, Baltimore, MD, USA.
Coello C., 1996, An empirical study of evolutionary techniques for multiobjective optimization in engineering design, Diss., Department of Computer Science, Tulane University.
Deb K., 1999, “Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems,” Evolutionary Computation, vol. 7, pp. 205–230.
Fonseca C. M. and Fleming P. J., 1998, “Multiobjective optimization and multiple constraint handling with evolutionary algorithms-Part I: a unified formulation,” IEEE Transactions on Systems, Man, & Cybernetics Part A: Systems & Humans, vol. 28, pp. 26–37.
Goldberg D., 1989, “Genetic Algorithms in Search and Machine Learning”. Reading, Addison Wesley.
Grueninger T. and Wallace D., 1996, “Multi-modal optimization using genetic algorithms”, MIT CADlab-Technical Report 96.02, http://cadlab.mit.edu/publications/.
Harik G., Finding multiple solutions in problems of bounded difficulty, IlliGAL Technical report No. 94002, http://www-illigal.ge.uiuc.edu/techreps.php3.
Hopsan, 1991, “Hopsan, a simulation package-User’s guide”, Technical report LiTH-IKPR-704, Department of Mechanical Engineering, Linköping University, Linköping, Sweden.
Horn J., 1997, “Multicriterion decision making,” in Handbook of evolutionary computation, T. Bäck, D. Fogel, and Z. Michalewicz, Eds., IOP Publishing Ltd.
11. Senin N., Wallace D., Borland N. and Jakiela M., 1999, “Distributed modeling and optimization of mixed variable design problems”, MIT CADlab-Technical Report: 99.01, http://cadlab.mit.edu/publications/.
Zitzler E. and Thiele L., 1999, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Transaction on evolutionary computation, vol. 3, pp. 257–271.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Andersson, J., Krus, P. (2001). Multiobjective Optimization of Mixed Variable Design Problems. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_44
Download citation
DOI: https://doi.org/10.1007/3-540-44719-9_44
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41745-3
Online ISBN: 978-3-540-44719-1
eBook Packages: Springer Book Archive