Optimal Design of a Speed Reducer through Particle Swarm

Authors

Keywords:

speed reducer, PSO with constrains, penalty function

Abstract

This article presents the optimization of the well-known Golinski speed reducer, through a modified particle swarm optimization algorithm (PSO), which includes a penalty system for considering the system restrictions. A good grade of similarity with results reported for both, traditional optimization methods and commercial software, was found. However and even though it isn’t critical, computation times were higher with PSO. The conclusion is that this PSO modification is a robust and mathematically simple solution for engineering optimization problems.

References

Agrawal,S. Parashar,K. W. English, and C. L. Bloebaum. “Web-based Visualization Framework for Decision making in Multidisciplinary Design Optimization”. The State University of New York Buffalo, 2004, pp. 21.

Budynas, N., (2006). “Mechanical Engineering: Shigley’s Mechanical Engineering Design”. Eighth Edition, McGraw-Hill, pp.348-383.

Cagnina, L. C., Esquivel, S. C. & Coello, C. A., (2008). “Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer”. Informática, Vol. XXXII, pp. 323–324.

Gao F, Yibo Qi, Qiang Yin, Jiaqing Xiao, “A- Novel Particle Swarm Optimization With Special Boundaries Restriction Strategy”. University of Technology, Wuhan, 430070, China, 2010, pp. 1-4.

Golinski, J. (1970). “Optimal Synthesis Problems Solved by Means of Nonlinear Programming and Random Methods”. Journal of Mechanisms, Vol V, pp. 287-309.

Grote, K-H. & Antonsson, E. K., (2008). “Springer Handbook of Mechanical Engineering”. Springer, pp. 334-398.

Jinn-Moon Yang, Ying-Ping Chen, Jorng-Tzong Horng, and ChengYan Kao, “Applying Family Competition to Evolution Strategies for Constrained Optimization”. National Taiwan University, Taipei, Taiwan, 2002, pp 207-208.

Konstantinos, E., Parsopoulos, K. E. & Vrahatis, M. N., (2010). “Particle swarm optimization and intelligence; advances and applications”. Information science reference, Hershey, New York, IGI global, pp.142-258.

Parsopoulos, K. E., Vrahatis, M. N., (2005). “Unified Particle Swarm Optimization in Dynamic Environments”. Lecture Notes in Computer Science (LNCS), Vol. MMMCDXLIX, Springer, pp. 590-599.

Parsopoulos, K. E., Vrahatis, M. N., (2007). “Parameter Selection and Adaptation in Unified Particle Swarm Optimization”. Mathematical and Computer Modelling, 46 (1-2), Elsevier, pp. 198-213.

Rao, S., (2009). “Classical Optimization Techniques”, in Engineering Optimization: Theory and Practice, J. Wiley, Ed. 4a. New Jersey: J. Wiley & Sons, pp. 446-483.

Ruben E. Perez and Kamran Behdinan, “Particle Swarm Optimization in Structural Design”. University of Toronto, Institute for Aerospace Studies, Ryerson University, Department of Aerospace Engineering Canada, 2007, pp. 376-377.

Sanchez, Francisco T., Pérez, Antonio, Sancho, Joaquin L., Rodríguez, Pablo J. (2006). “Mantenimiento Mecánico de Máquinas”. Publicationt de la Universitat Jaume I. pp. 54

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Published

2012-06-29

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Artículos

How to Cite

Optimal Design of a Speed Reducer through Particle Swarm. (2012). Entre Ciencia E ingeniería, 6(11), 183-199. https://ojs.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/693