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An Application of Multi-Objective Genetic Algorithm Based on Crossover Limited

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Advances in Intelligent Systems and Interactive Applications (IISA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 686))

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Abstract

Aimed at the problem of slow convergence of genetic algorithms, an improved genetic algorithm is given. The improved algorithm is based on the traditional NSGA, and uses crossover limited and elitist strategy to solve multiple objective network optimization. By these operations, the algorithm can effectively improve the speed of convergence. At the same times, the algorithm also uses objective layer method to select the non-dominated individuals. The current algorithm is used to solve the network optimum design problem with multiple objectives. Two objectives, the cost of path and delay of path are considered. A few examples for the network optimization generated by randomly are used test the algorithm. The result shows the algorithm can find better Pareto solutions.

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Acknowledgements

This work was supported by Tianjin Research Program of Application Foundation and Advanced Technology (14JCYBJC15400).

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Authors and Affiliations

  1. School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China

    Shi Lianshuan & Chen YinMei

Authors
  1. Shi Lianshuan
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  2. Chen YinMei
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Corresponding author

Correspondence to Shi Lianshuan .

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Editors and Affiliations

  1. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  2. Department of Computer Science and Engineering, Faculty of Engineering and Technology, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

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Lianshuan, S., YinMei, C. (2018). An Application of Multi-Objective Genetic Algorithm Based on Crossover Limited. In: Xhafa, F., Patnaik, S., Zomaya, A. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2017. Advances in Intelligent Systems and Computing, vol 686. Springer, Cham. https://doi.org/10.1007/978-3-319-69096-4_95

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  • DOI: https://doi.org/10.1007/978-3-319-69096-4_95

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69095-7

  • Online ISBN: 978-3-319-69096-4

  • eBook Packages: EngineeringEngineering (R0)

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