Abstract
In this paper, the behavior of different Particle Swarm Optimization (PSO) variants is analyzed when solving a set of well-known numerical constrained optimization problems. After identifying the most competitive one, some improvements are proposed to this variant regarding the parameter control and the constraint-handling mechanism. Furthermore, the on-line behavior of the improved PSO and some of the most competitive original variants are studied. Two performance measures are used to analyze the capabilities of each PSO to generate feasible solutions and to improve feasible solutions previously found i.e. how able is to move inside the feasible region of the search space. Finally, the performance of this improved PSO is compared against state-of-the-art PSO-based algorithms. Some conclusions regarding the behavior of PSO in constrained search spaces and the improved results presented by the modified PSO are given and the future work is established.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Eiben, A., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2006)
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics-Part B 26, 29–41 (1996)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Michalewicz, Z., Schoenauer, M.: Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation 4, 1–32 (1996)
Parsopoulos, K., Vrahatis, M.: Unified Particle Swarm Optimization for solving constrained engineering optimization problems. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 582–591. Springer, Heidelberg (2005)
Li, X., Tian, P., Kong, M.: Novel particle swarm optimization for constrained optimization problems. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1305–1310. Springer, Heidelberg (2005)
Krohling, R.A., dos Santos Coelho, L.: Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Transactions on Systems, Man and Cybernetics Part B 36, 1407–1416 (2006)
He, S., Prempain, E., Wu, Q.H.: An Improved Particle Swarm Optimizer for Mechanical Design Optimization Problems. Engineering Optimization 36, 585–605 (2004)
Toscano-Pulido, G., Coello Coello, C.A.: A Constraint-Handling Mechanism for Particle Swarm Optimization. In: Proceedings of the Congress on Evolutionary Computation 2004, Piscataway, New Jersey, vol. 2, pp. 1396–1403. IEEE Service Center, Los Alamitos (2004)
Liang, J.J., Suganthan, P.N.: Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constrain-Handling Mechanism. In: 2006 IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, BC, Canada, pp. 316–323. IEEE, Los Alamitos (2006)
Cagnina, L.C., Esquivel, S.C., Coello, C.A.C.: A Particle Swarm Optimizer for Constrained Numerical Optimization. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 910–919. Springer, Heidelberg (2006)
Lu, H., Chen, W.: Dynamic-objective particle swarm optimization for constrained optimization problems. Journal of Combinatorial Optimization 12, 409–419 (2006)
Deb, K.: An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186, 311–338 (2000)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)
Mezura-Montes, E., Coello, C.A.C.: Identifying On-line Behavior and Some Sources of Difficulty in Two Competitive Approaches for Constrained Optimization. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1477–1484. IEEE Press, Los Alamitos (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Flores-Mendoza, J.I., Mezura-Montes, E. (2008). Looking Inside Particle Swarm Optimization in Constrained Search Spaces. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-540-88636-5_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
eBook Packages: Computer ScienceComputer Science (R0)