Abstract
We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two perturbation operators: “c-perturbation” and “m-perturbation”. The goal of these operators is to prevent premature convergence and the poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules, enhanced with a dynamic ε-tolerance approach applicable to equality constraints. PESO is compared and outperforms highly competitive algorithms representative of the state of the art.
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
Mezura, E.: Alternatives to Handle Constraints in Evolutionary Optimization. PhD thesis, CINVESTAV-IPN, Mexico, DF (2004)
Parsopoulos, K., Vrahatis, M.: Particle swarm optimization method for constrained optimization problems. In: Proceedings of the Second Euro-International Symposium on Computational Intelligence, E-ISCI 2002 (2002)
Coath, G., Halgamuge, S.K.: A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems. In: Proceedings of the 2003 Congress on Evolutionary Computation, pp. 2419–2425. IEEE, Los Alamitos (2003)
Zhang, J., Xie, F.: Depso: Hybrid particle swarm with differential evolution operator. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 3816–3821. IEEE, Los Alamitos (2003)
Toscano, G., Coello, C.: A constraint-handling mechanism for particle swarm optimization. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 1396–1403. IEEE, Los Alamitos (2004)
Fieldsend, J., Singh, S.: A multi-objective algorithm based upon particle swarm optimization, and efficient data structure and turbulence. In: Proceedings of 2002 U.K. Workshop on Computational Intelligence, The European Network on Intelligent Technologies for Smart Adaptive Systems, pp. 37–44 (2002)
Kennedy, J.: Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. In: IEEE Congress on Evolutionary Computation, pp. 1931–1938. IEEE, Los Alamitos (1999)
Kennedy, J., Eberhart, R.: The Particle Swarm: Social Adaptation in Information-Processing Systems. McGraw-Hill, London (1999)
Runarsson, T., Yao, X.: Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews 35, 233–243 (2005)
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
Zavala, A.E.M., Aguirre, A.H., Diharce, E.R.V. (2005). Particle Evolutionary Swarm Optimization with Linearly Decreasing ε-Tolerance. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_65
Download citation
DOI: https://doi.org/10.1007/11579427_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29896-0
Online ISBN: 978-3-540-31653-4
eBook Packages: Computer ScienceComputer Science (R0)