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A Novel Weight-Based Immune Genetic Algorithm for Multiobjective Optimization Problems

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

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Abstract

The weight-based multiobjective evolutionary algorithms have been criticized mainly for the following aspects: (1) difficulty in finding Pareto-optimal solutions in problems having nonconvex Pareto-optimal region, and (2) non-elitism approach for most cases, and (3) difficulty in generating uniformly distributed Pareto-optimal solutions. In this paper, we propose a weight-based multiobjective immune genetic algorithm(MOIGA), which alleviates all the above three difficulties. In this proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function. An immune operator is adopted to increase the diversity of the population. Specifically, a new mate selection approach called tournament selection algorithm with similar individuals (TSASI) and a new environmental selection approach named truncation algorithm with similar individuals (TASI) are presented. Simulation results show MOIGA outperforms NSGA-II and RWGA.

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Authors and Affiliations

  1. Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, China

    Guixia He & Jiaquan Gao

Authors
  1. Guixia He
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  2. Jiaquan Gao
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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He, G., Gao, J. (2009). A Novel Weight-Based Immune Genetic Algorithm for Multiobjective Optimization Problems. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_57

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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