Abstract
This paper applies a genetic algorithm-based optimisation procedure, namely, NSGA-II, to the problem of synthesis of a four-bar mechanism. The internal parameters of \({\texttt{\rm NSGA-II}}\) are tuned using a Design of Experiments (DoE) procedure to enhance the quality of the final results. Constraints are handled through a penalty formulation. Further, a scaling function is introduced, which transforms the penalty terms in a manner that leads to faster convergence of the solutions. The theoretical developments are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis. A comparison of the results vis-a-vis existing ones shows that the proposed enhancements of the basic scheme of \({\texttt{\rm NSGA-II}}\) deliver promising improvements in terms of accuracy, and rate of convergence of the solutions.
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
Cabrera, J., Simon, A., Prado, M.: Optimal synthesis of mechanisms with genetic algorithms. Mechanism and Machine Theory 37(10), 1165–1177 (2002)
Laribi, M., Mlika, A., Romdhane, L., Zeghloul, S.: A combined genetic algorithm – fuzzy logic method (GA – FL) in mechanisms synthesis. Mechanism and Machine Theory 39(7), 717–735 (2004)
Acharyya, S., Mandal, M.: Performance of EAs for four-bar linkage synthesis. Mechanism and Machine Theory 44(9), 1784–1794 (2009)
Cabrera, J., Ortiz, A., Nadal, F., Castillo, J.: An evolutionary algorithm for path synthesis of mechanisms. Mechanism and Machine Theory 46(2), 127–141 (2011)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Montgomery, D.C.: Design and Analysis of Experiments. John Wiley & Sons (2006)
Mallik, A., Ghosh, A., Dittrich, G.: Kinematic Analysis and Synthesis of Mechanisms. CRC Press, Boca Raton (1994)
Ghosal, A.: Robotics: Fundamental Concepts and Analysis. Oxford University Press, New Delhi (2006)
Sarker, R., Mohammadian, M., Yao, X., Runarsson, T., Yao, X.: Constrained evolutionary optimization - the penalty function approach. In: Evolutionary Optimization. International Series in Operations Research & Management Science, vol. 48, pp. 87–113. Springer US (2003)
Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2-4), 311–338 (2000)
Rimon, E., Koditschek, D.E.: Exact robot navigation using artificial potential functions. IEEE Transactions on Robotics and Automation 8, 501–518 (1992)
Lin, Y., Mistree, F., Allen, J.K., Tsui, K.-L., Chen, V.C.P.: A sequential exploratory experimental design method: Development of appropriate empirical models in design, vol. 1, pp. 1021–1035 (2004)
Kunjur, A., Krishnamurty, S.: Genetic algorithms in mechanism synthesis. Journal of Applied Mechanisms and Robotics 4(2), 18–24 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Badduri, J., Srivatsan, R.A., Kumar, G.S., Bandyopadhyay, S. (2012). Coupler-Curve Synthesis of a Planar Four-Bar Mechanism Using NSGA-II. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-34859-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34858-7
Online ISBN: 978-3-642-34859-4
eBook Packages: Computer ScienceComputer Science (R0)