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International Work-Conference on Artificial Neural Networks

IWANN 2017: Advances in Computational Intelligence pp 40–51Cite as

IMOGA/SOM: An Intelligent Multi-objective Genetic Algorithm Using Self Organizing Map

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

Abstract

Multi-objective Genetic Algorithms (MOGAs) are probabilistic search techniques and provide solutions of multi-objective optimization problems. When MOGA reaches near optimal regions, it may face problem in convergence due to its probabilistic nature. MOGA does not pay attention on the neighbourhood of the current population which makes the convergence slow. This scenario may also lead to premature convergence. To overcome this problem, we propose an Intelligent Multi-objective Genetic Algorithm using Self Organizing Map (IMOGA/SOM). The proposed algorithm uses the neighbourhood property of SOM. SOM is trained by the solutions generated by MOGA. SOM performs competition and cooperation among its neurons for better convergence. We have compared the results of the proposed algorithm with two existing algorithms NSGA-II and SOM-Based Multi Objective Genetic Algorithm (SBMOGA). Empirical results demonstrate the superiority of the proposed algorithm IMOGA/SOM.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, 713209, West Bengal, India

    Subhradip Aon, Ashis Sau, Prasenjit Dey & Tandra Pal

Authors
  1. Subhradip Aon
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  2. Ashis Sau
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  3. Prasenjit Dey
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  4. Tandra Pal
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Corresponding author

Correspondence to Tandra Pal .

Editor information

Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Aon, S., Sau, A., Dey, P., Pal, T. (2017). IMOGA/SOM: An Intelligent Multi-objective Genetic Algorithm Using Self Organizing Map. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-59153-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59152-0

  • Online ISBN: 978-3-319-59153-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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