Abstract
This paper presents a novel hybrid algorithm by integrating particle swarm optimization with stochastic ranking for solving standard constrained engineering design problems. The proposed hybrid algorithm uses domain independent characteristics of stochastic ranking and faster convergence of particle swarm optimization. Performance comparison of the proposed algorithm with other popular techniques through comprehensive experimental investigations establishes the effectiveness and robustness of the proposed algorithm for solving engineering design problems.
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
Coelho, L.S.: Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications 37(2), 1676–1683 (2010)
Coello, C., Carlos, A.: Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41(2), 113–127 (2000)
Coello, C., Carlos, A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art. Comput. Meth. Appl. Mech. Eng. 191, 1245–1287 (2002)
Eberhart, R., Kenedy, J.: Particle swarm optimization. In: Proceedings of IEEE Int. Conference on Neural Networks, Piscataway, NJ, pp. 1114–1121 (November 1995)
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence 20(1), 89–99 (2007)
Huang, F.Z., Wang, L., He, Q.: An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation 186(1), 340–356 (2007)
Jaberipour, M., Khorram, E.: Two improved harmony search algorithms for solving engineering optimization problems. Communications in Nonlinear Science and Numerical Simulation 15(11), 3316–3331 (2010)
Mallipeddi, R., Suganthan, P.: Ensemble of constraint handling techniques. IEEE Transactions on Evolutionary Computation 14(4), 561–579 (2010)
Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Journal of Evolutionary Computation 4(1), 1–32 (1996)
Ray, T., Liew, K.M.: Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation 7(4), 386–396 (2003)
Sabat, S.L., Ali, L.: The hyperspherical acceleration effect particle swarm optimizer. Appl. Soft. Computing 9(13), 906–917 (2008)
Sabat, S.L., Ali, L., Udgata, S.K.: Adaptive accelerated exploration particle swarm optimizer for global multimodal functions. In: World Congress on Nature and Biologically Inspired Computing, Coimbatore, India, pp. 654–659 (December 2009)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)
Takahama, T., Sakai, S.: Solving constrained optimization problems by the ε constrained particle swarm optimizer with adaptive velocity limit control. In: IEEE Congress on Evolution Computation, Vancouver, BC, Canada, pp. 308–315 (July 2006)
Yang, B., Chen, Y., Zhao, Z., Han, Q.: A master-slave particle swarm optimization algorithm for solving constrained optimization problems. In: 6th Congress on Intelligent Control and Automation, Dalian, pp. 3208–3212 (2006)
Zahara, E., Kao, Y.-T.: Hybrid nelder-mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Systems with Applications 36(2, Part 2), 3880–3886 (2009)
Zhang, M., Luo, W., Wang, X.: Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences 178(15), 3043–3074 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sabat, S.L., Ali, L., Udgata, S.K. (2010). Stochastic Ranking Particle Swarm Optimization for Constrained Engineering Design Problems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-17563-3_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17562-6
Online ISBN: 978-3-642-17563-3
eBook Packages: Computer ScienceComputer Science (R0)