Abstract
We investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems. For this purpose, a penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated on four well–known engineering problems with promising results. Comparisons with the standard local and global variant of Particle Swarm Optimization are reported and discussed.
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
Floudas, C.A., Pardalos, P.M.: A collection of test problems for constrained global optimization algorithms. In: Floudas, C.A., Pardalos, P.M. (eds.) A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)
Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)
Coello Coello, C.A.: A survey of constraint handling techniques used with evolutionary algorithms. Techn. Rep. Lania–RI–99–04, LANIA (1999)
Hu, X., Eberhart, R.C., Shi, Y.: Engineering optimization with particle swarm. In: Proc. 2003 IEEE Swarm Intelligence Symposium, pp. 53–57 (2003)
Joines, J.A., Houck, C.R.: On the use of non–stationary penalty functions to solve nonlinear constrained optimization problems with ga’s. In: Proc. IEEE Int. Conf. Evol. Comp., pp. 579–585 (1994)
Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. In: Sincak, et al. (eds.) Intelligent Technologies–Theory and Application: New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 214–220. IOS Press, Amsterdam (2002)
Yeniay, Ö.: Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications 10, 45–56 (2005)
Coello Coello, C.A.: Self–adaptive penalties for ga–based optimization. In: Proc. 1999 IEEE CEC, Washington, D.C., USA, vol. 1, pp. 573–580 (1999)
Coello Coello, C.A.: Use of a self–adaptive penalty approach for engineering optimization problems. Computers in Industry 41, 113–127 (2000)
Parsopoulos, K.E., Vrahatis, M.N.: UPSO: A unified particle swarm optimization scheme. In: Proc. Int. Conf. Computational Methods in Sciences and Engineering (ICCMSE 2004). Lecture Series on Computer and Computational Sciences, vol. 1, pp. 868–873. VSP International Science Publishers, Zeist (2004)
Parsopoulos, K.E., Vrahatis, M.N.: Unified particle swarm optimization in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 590–599. Springer, Heidelberg (2005)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings Sixth Symposium on Micro Machine and Human Science, Piscataway, NJ, pp. 39–43. IEEE Service Center, Los Alamitos (1995)
Clerc, M., Kennedy, J.: The particle swarm–explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)
Arora, J.S.: Introduction to Optimum Design. McGraw-Hill, New York (1989)
Rao, S.S.: Engineering Optimization–Theory and Practice. Wiley, Chichester (1996)
Sandgen, E.: Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design (ASME) 112, 223–229 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parsopoulos, K.E., Vrahatis, M.N. (2005). Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_71
Download citation
DOI: https://doi.org/10.1007/11539902_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)