Skip to main content
SpringerLink
Log in

MCGA: A Multiobjective Cellular Genetic Algorithm Based on a 3D Grid

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

This paper proposes a new cellular multiobjective genetic algorithm based on a 3D grid structure. The basic idea is to organize the candidate solutions by a 3D grid, and the reproduction and replacement operators are based on the 3D grid. The proposed algorithm is compared with two 2D cellular multiobjective genetic algorithms on the DTLZ test suite, and the statistical results indicate that our approach performs better than the compared algorithms according to both the diversity and convergence metrics. Furthermore, our approach is computational more stable.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  2. Miettinen, K.M.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers (1999)

    Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, A., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  4. Kim, M., Hiroyasu, T., Miki, M., Watanabe, S.: SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 742–751. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Knowles, J., Corne, D.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. In: Proceedings of the 1999 IEEE Congress on Evolutionary Computation (CEC 1999), pp. 98–105. IEEE (1999)

    Google Scholar 

  6. Alba, E., Dorronsoro, B., Luna, F., et al.: A Cellular Multi-objective Genetic Algorithm for Optimal Broadcasting Strategy in Metropolitan MANETs. Computer Communications 30(4), 685–697 (2007)

    Article  Google Scholar 

  7. Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: Design Issues in a Multiobjective Cellular Genetic Algorithm. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 126–140. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Zhang, Q.F., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transation on Evolutionary Computation 11(6), 712–731 (2007)

    Article  Google Scholar 

  9. Durillo, J.J., Nebro, A.J., Luna, F., Alba, E.: Solving Three-Objective Optimization Oroblems Using a New Hybrid Cellular Genetic Algorithm. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N., et al. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 661–670. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Abla, E., Dorronsoro, B.: Cellular Genetic Algorithms. Springer, Berlin (2008)

    Google Scholar 

  11. Al-Naqi, A., Erdogan, A.T., Arslan, T.: Balancing Exploration and Exploitation in an Adaptive Three-dimensional Cellular Genetic Algorithm via a Probabilistic Selection operator. In: Proceedings of 2010 NASA/ESA Conference on Adaptive Hardware and Systems, pp. 258–264. IEEE Computer Society (2010)

    Google Scholar 

  12. Al-Naqi, A., Erdogan, A.T., Arslan, T.: Fault Tolerance Through Automatic Cell Isolation Using Three-dimensional Cellular Genetic Algorithms. In: Proceedings of 2010 IEEE Congress on Evolutionary Computation (2010)

    Google Scholar 

  13. Al-Naqi, A., Erdogan, A.T., Arslan, T.: Fault Tolerant Three-dimensional Cellular Genetic Algorithms with Adaptive Migration Achemes. In: Proceedings of 2011 NASA/ESA Conference on Adaptive Hardware and Systems, pp. 352–359. IEEE Computer Society (2011)

    Google Scholar 

  14. Deb, K., Thiele, L., Laumanns, M., et al.: Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Proceedings of the Evolutionary Multiobjective Optimization, pp. 105–145. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Durillo, B.J.J.: Metaheuristics for Multi-objective Optimization: Design, Analysis, and Applications. University of Malaga, Spain (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, 150001, China

    Hu Zhang & Shenming Song

  2. Department of Computer Science and Technology, East China Normal University, Shanghai, 200235, China

    Aimin Zhou

Authors
  1. Hu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shenming Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aimin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, H., Song, S., Zhou, A. (2013). MCGA: A Multiobjective Cellular Genetic Algorithm Based on a 3D Grid. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41278-3_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation