Skip to main content
Springer Nature Link
Log in

Task Decomposition for Optimization Problem Solving

  • Conference paper
Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

Included in the following conference series:

  • 1508 Accesses

Abstract

This paper examines a new way of dividing computational tasks into smaller interacting components, in order to effectively solve constrained optimization problems. In dividing the tasks, we propose problem decomposition, and the use of GAs as the solution approach. In this paper, we consider problems with block angular structures with or without overlapping variables. We decompose not only the problem but also appropriately the chromosome for different components of the problem. We also design a communication process for exchanging information between the components. The approach can be implemented for solving large scale optimization problems using parallel machines. A number of test problems have been solved to demonstrate the use of the proposed approach. The results are very encouraging.

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. Conn, A.R., Gould, N.I.M., Toint, P.L.: Large-scale nonlinear constrained optimization: a current survey. In: Shanno, D.F., Dixon, L., Spedicato, E. (eds.) Algorithms for continuous optimization: the state of the art, vol. 434, pp. 287–332. Kluwer Academic Publishers Group (1994)

    Google Scholar 

  2. Elfeky, E.Z., Sarker, R.A., Essam, D.L.: Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization. Journal of Computer Science And Technology 23(1), 19–34 (2008)

    Article  Google Scholar 

  3. Martin, R.K.: Large Scale Linear and Integer Optimization: A Unified Approach. Springer, Heidelberg (1998)

    Google Scholar 

  4. Kato, K., Sakawa, M.: Genetic algorithms with decomposition procedures for multidimensional 0-1 knapsack problems with block angular structures. IEEE Transactions on Systems, Man, and Cybernetics, Part B 33(3), 410–419 (2003)

    Article  Google Scholar 

  5. Lin, S.-S., Chang, H.: A Decomposition-Technique-Based Algorithm for Nonlinear Large Scale Mesh-Interconnected System and Application. IEICE Trans. Fundamentals E89-A(10), 2847–2856 (2006)

    Article  Google Scholar 

  6. Benjamin, W.W., Yixin, C., Andrew, W.: Constrained Global Optimization by Constraint Partitioning and Simulated Annealing. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  7. Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178(15), 2985–2999 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)

    MATH  Google Scholar 

  9. Dembo, R.S.: A set of geometric programming test problems and their solutions. Mathematical Programming 10(1), 192–213 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  10. Floudas, C.A., Pardalos, P.M.: A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)

    MATH  Google Scholar 

  11. Koziel, S., Michalewicz, Z.: Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization. Evolutionary Computation 7(1), 19–44 (1999)

    Article  Google Scholar 

  12. Hock, W., Schittkowski, K.: Text examples for nonlinear programming codes. Springer, New York (1981)

    Book  MATH  Google Scholar 

  13. Elfeky Ehab, Z., Sarker Ruhul, A., Essam, D.L.: Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization. Journal of Computer Science And Technology 23(1), 19–34 (2008)

    Article  Google Scholar 

  14. Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  15. Elfeky, E.Z., Sarker, R.A., Essam, D.L.: A Simple Ranking and Selection for Constrained Evolutionary Optimization. In: Wang, T.-D., Li, X.-D., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 537–544. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of ITEE, University of New South Wales at ADFA, Canberra, Australia

    Ehab Z. Elfeky, Ruhul A. Sarker & Daryl L. Essam

Authors
  1. Ehab Z. Elfeky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruhul A. Sarker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daryl L. Essam
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

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

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Elfeky, E.Z., Sarker, R.A., Essam, D.L. (2008). Task Decomposition for Optimization Problem Solving. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89694-4_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics