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Evolutionary Canonical Particle Swarm Optimizer – A Proposal of Meta-optimization in Model Selection

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Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5163))

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Abstract

We proposed Evolutionary Particle Swarm Optimization (EPSO) which provides a new paradigm of meta-optimization for model selection in swarm intelligence. In this paper, we extend the technique of online evolutionary computation of EPSO to Canonical Particle Swarm Optimizer (CPSO), and propose Evolutionary Canonical Particle Swarm Optimizer (ECPSO) for optimizing CPSO. In order to effectually evaluate the performance of CPSO, a temporally cumulative fitness function of the best particle is adopted in ECPSO as the behavioral representative for entire swarm. Applications of the proposed method to a suite of 5-dimensional benchmark problems well demonstrate the effectiveness. Our experimental results clearly indicate that (1) the proper parameter sets in CPSO for solving various optimization problems are not unique; (2) the values of parameters in them are quite different from that of the original CPSO; (3) the search performance of the optimized CPSO is superior to that of the original CPSO, and to that of RGA/E except for the result to the Rastrigin’s benchmark problem.

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References

  1. Beielstein, T., Parsopoulos, K.E., Vrahatis, M.N.: Tuning PSO Parameters Through Sensitivity Analysis, Technical Report of the Collaborative Research Center 531 Computational Intelligence CI-124/02, University of Dortmund (2002)

    Google Scholar 

  2. Carlisle, A., Dozier, G.: An Off-The-Shelf PSO. In: The Workshop on Particle Swarm Optimization, Indianapolis, pp. 1–6 (2001)

    Google Scholar 

  3. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2000)

    Article  Google Scholar 

  4. Clerc, M.: Particle Swarm Optimization. Iste Publishing Co., UK (2006)

    MATH  Google Scholar 

  5. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: The sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)

    Google Scholar 

  6. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particleswarm optimization. In: The 2000 IEEE Congress on Evolutionary Computation, La Jolla, CA, vol. 1, pp. 84–88 (2000)

    Google Scholar 

  7. Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann Publishers, CA (2001)

    Google Scholar 

  8. Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man and Cybernetics, Part B 35(6), 1272–1282 (2005)

    Article  Google Scholar 

  9. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: The 1995 IEEE International Conference on Neural Networks, Piscataway, New Jersey, pp. 1942–1948 (1995)

    Google Scholar 

  10. Kennedy, J.: In Search of the Essential Particle Swarm. In: The 2006 IEEE Congress on Evolutionary Computations, Vancouver, BC, Canada, pp. 6158–6165 (2006)

    Google Scholar 

  11. Meissner, M., Schmuker, M., Schneider, G.: Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 7(125) (2006)

    Google Scholar 

  12. Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through Particle Swarm Optimization. Natural Computing 1, 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  13. Pasupuleti, P., Battiti, R.: The Gregarious Recent Particle Swarm Optimizer (G-PSO). In: IEEE Congress on Evolutionary Computation, pp. 84–88 (2000)

    Google Scholar 

  14. Reyes-Sierra, M., Coello, C.A.C.: Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  15. Spina, R.: Optimisation of injection moulded parts by using ANN-PSO approach. Journal of Achievements in Materials and Manufacturing Engineering 15(1-2), 146–152 (2006)

    Google Scholar 

  16. Xie, X.-F., Zhang, W.-J., Yang, Z.-L.: A Dissipative Particle Swarm Optimization. In: The IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, USA, pp. 1456–1461 (2002)

    Google Scholar 

  17. Zhang, H., Ishikawa, M.: Evolutionary Particle Swarm Optimization (EPSO) – Estimation of Optimal PSO Parameters by GA. In: The IAENG International MultiConference of Engineers and Computer Scientists (IMECS 2007), Newswood Limited, Hong Kong, China, vol. 1, pp. 13–18 (2007)

    Google Scholar 

  18. Zhang, H., Ishikawa, M.: Designing Particle Swarm Optimization – Performance Comparison of Two Temporally Cumulative Fitness Functions in EPSO. In: 26th IASTED International Conference on Artificial Intelligence and Applications (AIA 2008), Innsbruck, Austria, pp. 301–306 (2008)

    Google Scholar 

  19. Zhang, H., Ishikawa, M.: Evolutionary Particle Swarm Optimization – Metaoptimization Method with GA for Estimating Optimal PSO Methods. In: Castillo, O., et al. (eds.) Trends in Intelligent Systems and Computer Engineering. Lecture Notes in Electrical Engineering, vol. 6, pp. 75–90. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Brain Science & Engineering, Kyushu Institute of Technology,  , 2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan

    Hong Zhang & Masumi Ishikawa

  2. Department of Brain Science and Engineering, Graduate School of Life Science & Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan

    Hong Zhang

Authors
  1. Hong Zhang
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  2. Masumi Ishikawa
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Véra Kůrková Roman Neruda Jan Koutník

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Zhang, H., Ishikawa, M. (2008). Evolutionary Canonical Particle Swarm Optimizer – A Proposal of Meta-optimization in Model Selection. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_49

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  • DOI: https://doi.org/10.1007/978-3-540-87536-9_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87535-2

  • Online ISBN: 978-3-540-87536-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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