Land Combat Scenario Planning: A Multiobjective Approach | SpringerLink
Skip to main content
Springer Nature Link
Log in

Land Combat Scenario Planning: A Multiobjective Approach

  • Conference paper
Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

Included in the following conference series:

Abstract

The simulation of land combat operations is a complex task. The space of possibilities is exponential and the performance criteria are usually in conflict; thus finding a sweet spot in this complex search space is a hard task. This paper focuses on the effect of population size and mutation rate on the performance of NSGA–II, as the evolutionary multiobjective optimization technique, to decide on the composition of forces using a complex land combat multi-agent scenario planning tool.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ursem, R.K.: Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization. Ph.d. thesis, University of Aarhus, Denmark (2003)

    Google Scholar 

  2. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  3. Yang, A., Abbass, H.A., Sarker, R.: WISDOM-II: A network centric model for warfare. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3683, Springer, Heidelberg (2005)

    Google Scholar 

  4. Yang, A., Abbass, H.A., Sarker, R.: Landscape dynamics in multi-agent simulation combat systems. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, Springer, Heidelberg (2004)

    Google Scholar 

  5. Yang, A., Abbass, H.A., Sarker, R., Curtis, N.J.: Evolving capability requirements in WISDOM-II. In: Abbass, H.A., Bossamier, T., Wiles, J. (eds.) Advances in Artificial Life, Proceeding of The Second Australian Conference on Artificial Life (ACAL 2005), Sydney, Australia, pp. 335–348. World Scientific Publisher, Singapore (2005)

    Google Scholar 

  6. Deb, K., Agrawal, R.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)

    MATH  MathSciNet  Google Scholar 

  7. Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26, 30–45 (1996)

    Google Scholar 

  8. Deb, K., Agrawal, S.: Understanding interactions among genetic algorithm parameters. In: Banzhaf, W., Reeves, C. (eds.) Foundations of Genetic Algorithms 5, pp. 265–286. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  9. Ballester, P.J., Carter, J.N.: Real-parameter genetic algorithms for finding multiple optimal solutions in multi-modal optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 706–717. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Khare, V., Yao, X., Deb, K.: Performance scaling of multi-objective evolutionary algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  11. Iorio, A., Li, X.: A cooperative coevolutionary multiobjective algorithm using non-dominated sorting. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 537–548. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Zitzler, E., Thiele, L., Deb, K.: Comparision of multiobjective evolutionary algorithms: Emprical results. Evolutionary Computation 8, 173195 (2000)

    Article  Google Scholar 

  13. Fonseca, C.M., Fleming, P.J.: On the performance assessment and comparison of stochastic multiobjective optimizers. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 584–593. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  14. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 8, 149–172 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Defence and Security Applications Research Centre (DSA), University of New South Wales, Australian Defence Force Academy, Canberra, ACT, 2600, Australia

    Ang Yang, Hussein A. Abbass & Ruhul Sarker

Authors
  1. Ang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hussein A. Abbass
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruhul Sarker
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, A., Abbass, H.A., Sarker, R. (2006). Land Combat Scenario Planning: A Multiobjective Approach. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_105

Download citation

  • DOI: https://doi.org/10.1007/11903697_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics