Significant Factors Identification for Particle Swarm Optimization Algorithm to Solving the Design Optimization Problem of a Four-Bar Linkage for Path Generation

Abstract:

Particle swarm optimization (PSO) is a well-known population-based searching algorithm to solving optimization problems. This paper aims at identifying significant control factors for PSO to solving the design optimization problem of a four-bar linkage for path generation. Control factors considered herein are inertial weight, acceleration coefficients, breeding operation, and the number of population. A full factorial design of experiments is used to construct a set of experiments. Experimental results are analyzed with the analysis of variance method. According to the results obtained in this paper, breeding operation and the interaction between breeding operation and acceleration coefficients are significant. Inertial weight, acceleration coefficients, the number of population, and the other interactions are not significant. For the design optimization problem discussed herein, it is suggested to adopt breeding operation strategy and apply constant acceleration coefficients to increase significantly PSO’s performance and robustness. Type of inertial weight and the number of population do not affect PSO’s performance and robustness significantly.

• Periodical: Applied Mechanics and Materials (Volumes 249-250)
• Pages: 1180-1187
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.249-250.1180
• Online since: December 2012
• Authors: Cheng Kang Lee, Yung Chang Cheng
• Keywords: Analysis of Variance (ANOVA), Four-Bar Linkage, Mechanism, Particle Swarm Optimization (PSO), Path Generation, Significance Analysis

References

[1] F. Freudenstein, An analytical approach to the design of four-link mechanisms, Transactions of the ASME 76 (1954) 483-492.

[2] J.A. Cabrera, A. Simon, M. Prado, Optimal synthesis of mechanisms with genetic algorithms, Mechanism and Machine Theory 37 (2002) 1165-1177.

DOI: 10.1016/s0094-114x(02)00051-4

[3] S.K. Acharyya and M. Mandal, Performance of EAs for four-bar linkage synthesis, Mechanism and Machine Theory 44 (2009) 1784-1794.

DOI: 10.1016/j.mechmachtheory.2009.03.003

[4] W.Y. Lin, A GA-DE hybrid evolutionary algorithm for path synthesis of 4-bar linkage, Mechanism and Machine Theory 45 (2010) 1096-1107.

DOI: 10.1016/j.mechmachtheory.2010.03.011

[5] J. Kennedy and R.C. Eberhart, Particle swarm optimization, in: Proc. IEEE International Conference on Neural Networks, 1995, p.1942–(1948).

[6] Y. Shi and R.C. Eberhart., A modified particle swarm optimizer, in: Proc. IEEE International Conference on Evolutionary Computation, 1998, p.69–73.

DOI: 10.1109/icec.1998.699146

[7] Y. Shi and R.C. Eberhart, Empirical study of particle swarm optimization, in: Proc. IEEE International Congress on Evolutionary Computation, 1999, p.101–106.

[8] R.C. Eberhart and Y. Shi, Tracking and optimizing dynamic systems with particle swarms, in: Proc. IEEE Congress on Evolutionary Computation, 2001, p.94–97.

DOI: 10.1109/cec.2001.934376

[9] G. Chen, X. Huang, J. Jia and Z. Min, Natural exponential inertia weight strategy in particle swarm optimization, in: The Sixth World Congress on Intelligent Control and Automation, 2006, pp.3672-3675.

DOI: 10.1109/wcica.2006.1713055

[10] A. Ratnaweera, S.K. Halgamuge and H.C. Watson, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transactions on Evolutionary Computation 8 (2004) 240-255.

DOI: 10.1109/tevc.2004.826071

[11] M. Lovbjerg, T.K. Rasmussen and T. Krink, Hybrid particle swarm optimizer with breeding and subpopulations, in: Proc. 3rd Genetic and Evolutionary Computation Conference, 2001, pp.469-476.

[12] Z.M. Meng and D.G. Chang, Mechanical Design Optimization Technology with Applications, China Machine Press, Beijing, 2008. (in Chinese).

[13] C. Gong and Z.L. Wang, Mastering MATLAB Optimization Calculation, Publishing House of Electronics Industry, Beijing, 2009. (in Chinese).