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Modern Metaheuristics for Function Optimization Problem

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Intelligent Information Processing and Web Mining

Part of the book series: Advances in Soft Computing ((AINSC,volume 31))

Abstract

This paper compares the behaviour of three metaheuristics for the function optimization problem on a set of classical functions handling a lot number of variables and known to be hard. The first algorithm to be described is Particle Swarm Optimization (PSO). The second one is based on the paradigm of Artificial Immune System (AIS). Both algorithms are then compared with a Genetic Algorithm (GA). New insights on how these algorithms behave on a set of difficult objective functions with a lot number of variables are provided.

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References

  1. Leonardo N. de Castro, Fernando J. Von Zuben (June 2002) “Learning and Optimization Using the Clonal Selection Principle”, IEEE Transaction on Evolutionary Computation, vol. 6. no. 3

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  2. Liping Zhang, Huanjun Yu, and Shangxu Hu (2003) “A New Approach to Improve Particle Swarm Optimization”, GECCO 2003

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  3. Michalewicz Z. (1996) “Genetic Algorithms + Data Structures = Evolution Programs”, Springer

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  4. Shi Y. and Eberhart, R. (2000) “Experimental study of particle swarm optimization”, Proc. SCI2000 Conference, Orlando, FL

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  5. Shi Y. and Eberhart R. (2001) “Fuzzy adaptive particle swarm optimization”, Proceedings of the 2001 Congress on Evolutionary Computation

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Academy of Podlasie, Sienkiewicza 51, 08-110, Siedlce, Poland

    Marek Pilski & Franciszek Seredyński

  2. Faculty of Sciences, Technology and Communication, University of Luxembourg, 6, rue Coudenhove Kalergi, L1359, Luxembourg-Kirchberg, Luxembourg

    Pascal Bouvry

  3. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Franciszek Seredyński

  4. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    Franciszek Seredyński

Authors
  1. Marek Pilski
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  2. Pascal Bouvry
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  3. Franciszek Seredyński
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Editor information

Editors and Affiliations

  1. Institute of Computer Sciences, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warszawa, Poland

    Mieczysław A. Kłopotek , Sławomir T. Wierzchoń  & Krzysztof Trojanowski ,  & 

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© 2005 Springer-Verlag Berlin Heidelberg

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Pilski, M., Bouvry, P., Seredyński, F. (2005). Modern Metaheuristics for Function Optimization Problem. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_54

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  • DOI: https://doi.org/10.1007/3-540-32392-9_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25056-2

  • Online ISBN: 978-3-540-32392-1

  • eBook Packages: EngineeringEngineering (R0)

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