Advertisement

Particle Evolutionary Swarm Optimization with Linearly Decreasing ε-Tolerance

  • Angel E. Muñoz Zavala
  • Arturo Hernández Aguirre
  • Enrique R. Villa Diharce
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3789)

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.

Keywords

Particle Swarm Optimization Equality Constraint Particle Swarm Optimization Algorithm Constraint Handling Reproduction Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mezura, E.: Alternatives to Handle Constraints in Evolutionary Optimization. PhD thesis, CINVESTAV-IPN, Mexico, DF (2004)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: The Particle Swarm: Social Adaptation in Information-Processing Systems. McGraw-Hill, London (1999)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Angel E. Muñoz Zavala
    • 1
  • Arturo Hernández Aguirre
    • 1
  • Enrique R. Villa Diharce
    • 1
  1. 1.Department of Computer ScienceCenter for Research in Mathematics (CIMAT)México

Personalised recommendations