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Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance

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Evolutionary Multi-Criterion Optimization (EMO 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3410))

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Abstract

In this paper, we propose a new Multi-Objective Particle Swarm Optimizer, which is based on Pareto dominance and the use of a crowding factor to filter out the list of available leaders. We also propose the use of different mutation (or turbulence) operators which act on different subdivisions of the swarm. Finally, the proposed approach also incorporates the ∈-dominance concept to fix the size of the set of final solutions produced by the algorithm. Our approach is compared against five state-of-the-art algorithms, including three PSO-based approaches recently proposed. The results indicate that the proposed approach is highly competitive, being able to approximate the front even in cases where all the other PSO-based approaches fail.

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References

  1. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  2. Coello Coello, C.A., Toscano Pulido, G., Salazar Lechuga, M.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8, 256–279 (2004)

    Article  Google Scholar 

  3. Mostaghim, S., Teich, J.: Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization (MOPSO). In: 2003 IEEE Swarm Intelligence Symposium Proceedings, Indianapolis, Indiana, USA, pp. 26–33. IEEE Service Center, Los Alamitos (2003)

    Google Scholar 

  4. Ray, T., Liew, K.: A Swarm Metaphor for Multiobjective Design Optimization. Engineering Optimization 34, 141–153 (2002)

    Article  Google Scholar 

  5. Hu, X., Eberhart, R.: Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization. In: Congress on Evolutionary Computation (CEC 2002), Piscataway, New Jersey, vol. 2, pp. 1677–1681. IEEE Service Center, Los Alamitos (2002)

    Google Scholar 

  6. Hu, X., Eberhart, R.C., Shi, Y.: Particle Swarm with Extended Memory for Multiobjective Optimization. In: 2003 IEEE Swarm Intelligence Symposium Proceedings, pp. 193–197. IEEE Service Center, Indianapolis (2003)

    Google Scholar 

  7. Fieldsend, J.E., Singh, S.: A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and Turbulence. In: Proceedings of the 2002 U.K. Workshop on Computational Intelligence, Birmingham, UK, pp. 37–44 (2002)

    Google Scholar 

  8. Toscano Pulido, G., Coello Coello, C.A.: Using Clustering Techniques to Improve the Performance of a Particle Swarm Optimizer. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 225–237. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Mostaghim, S., Teich, J.: The role of ε-dominance in multi objective particle swarm optimization methods. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), vol. 3, pp. 1764–1771. IEEE Press, Canberra (2003)

    Chapter  Google Scholar 

  10. Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10, 263–282 (2002)

    Article  Google Scholar 

  11. Mostaghim, S., Teich, J.: Covering pareto-optimal fronts by subswarms in multi-objective particle swarm optimization. In: 2004 Congress on Evolutionary Computation (CEC’2004), vol. 2, pp. 1404–1411. IEEE Computer Society Press, Portland (2004)

    Google Scholar 

  12. Li, X.: A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 37–48. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  13. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  14. Li, X.: Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 117–128. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Bäck, T. (ed.): Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)

    MATH  Google Scholar 

  16. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: Giannakkoglou, K.C., et al. (eds.) Proceedings of the EUROGEN 2001 Conference, Barcelona, Spain,CIMNE (2002) pp. 95–100 (2002)

    Google Scholar 

  17. Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms—A Comparative Study. In: Eiben, A.E. (ed.) Parallel Problem Solving from Nature V, Amsterdam, pp. 292–301. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  18. Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (1999)

    Google Scholar 

  19. Reyes-Sierra, M., Coello, C.A.C.: A New Multi-Objective Particle Swarm Optimizer with Improved Selection and Diversity Mechanisms. Technical Report EVOCINV-05-2004, Sección de Computación, Depto. de Ingeniería Eléctrica, CINVESTAV-IPN, México (2004)

    Google Scholar 

  20. Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8, 173–195 (2000)

    Article  Google Scholar 

  21. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Congress on Evolutionary Computation (CEC’2002), vol. 1, pp. 825–830. IEEE Computer Society Press, Piscataway (2002)

    Google Scholar 

  22. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7, 117–132 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical Eng. Department, Computer Science Dept., CINVESTAV-IPN (Evolutionary Computation Group), Av. IPN No. 2508, Col. San Pedro Zacatenco, México D.F., 07300, México

    Margarita Reyes Sierra & Carlos A. Coello Coello

Authors
  1. Margarita Reyes Sierra
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  2. Carlos A. Coello Coello
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Editor information

Editors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), 07300, México, D.F., México

    Carlos A. Coello Coello

  2. Centro de Investigación en Matemáticas (CIMAT), A.P. 402, Guanajuato, 36000, Mexico

    Arturo Hernández Aguirre

  3. Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland

    Eckart Zitzler

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© 2005 Springer-Verlag Berlin Heidelberg

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Sierra, M.R., Coello Coello, C.A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_35

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  • DOI: https://doi.org/10.1007/978-3-540-31880-4_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24983-2

  • Online ISBN: 978-3-540-31880-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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