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Fuzzy preference-based multi-objective optimization method

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Abstract

Multiobjective evolutionary computation is still quite young and there are many open research problems. This paper is an attempt to design a hybridized Multiobjective Evolutionary Optimization Algorithm with fuzzy logic called Fuzzy Preference-Based Multi–Objective Optimization Method (FPMOM). FPMOM as an integrated components of Multiobjective Optimization Technique, Evolutionary Algorithm and Fuzzy Inference System able to search and filter the pareto-optimal and provide a good trade-off solution for the multiobjective problem using fuzzy inference method to choose the user intuitive based specific trade-off requirement. This paper will provide a new insight into the behaviourism of interactive Multiobjective Evolutionary Algorithm optimization problems using fuzzy inference method.

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References

  • Cohon JL (1985) Multicriteria programming: Brief review and application. In: Gero JS (ed) Design optimization. Academic Press, New York, pp 163–191

    Google Scholar 

  • Coello CAC (1999) An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends. In: Congress on evolutionary computation (CEC99), vol 1, Piscataway, NJ, pp 3–13. IEEE

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, London

    MATH  Google Scholar 

  • Deb K, Sundar J, Udaya Bhaskara Rao N, Chaudhuri S (2006) Reference point based multi-objective optimization using evolutionary algorithms. Int J Comput Intell Res, ISSN 0973-1873, 2(3):273–286

  • Earl C (1998) The fuzzy systems handbook, 2nd edn. A practitioner’s guide to building using and maintaining fuzzy systems. Ap Professional, Book & Disk edition

  • Eckart Z (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. Computer Engineering and networks Laboratory, Doctoral Dissertation, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland

  • Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3(1): 1–16

    Article  Google Scholar 

  • Fonseca CM, Fleming PJ (1996) On the performance assessment and comparison of stochastic multiobjective optimizers. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Fourth international conference on parallel problem solving from nature (PPSN-IV). Springer, Berlin, pp 584–593

    Chapter  Google Scholar 

  • Horn J, Goldberg DE, Deb K (1994) Implicit niching in a learning classifier syse: Nature’s way Technicl report llliGAL report No.94001. University of Ilinois at Urbana Champaign

  • Jiro K, Tomoyuki H, Mitsumori M, Shinya W (2001) MOGADES: multi-objective genetic algorithm with distributed environment scheme. Department Of Knowledge Enginineering from Doshiha University, Japan

    Google Scholar 

  • Pohlheim H (2000) Greatbx [Online],[Accessed Nov 2000]. Available from World Wide Web: http://www.greatbx.com, 1994–2000

  • The Mathworks (2000) Fuzzy logic toolbox for use with matlab. User’s guide ver2.0

  • The Mathworks (2001) Genetic algorithm toolbox for use with matlab. User’s guide ver 1.2

  • Vilem N, Irina P (2000) Discovering the world with fuzzy logic. Springer, Berlin

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Materials and Mineral Resources Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Sivakumar Ramakrishnan

  2. School of Mathematical Science, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Yahya Abu Hasan

Authors
  1. Sivakumar Ramakrishnan
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  2. Yahya Abu Hasan
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Correspondence to Sivakumar Ramakrishnan.

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Ramakrishnan, S., Hasan, Y.A. Fuzzy preference-based multi-objective optimization method. Artif Intell Rev 39, 165–181 (2013). https://doi.org/10.1007/s10462-011-9264-4

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  • DOI: https://doi.org/10.1007/s10462-011-9264-4

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