Skip to main content
SpringerLink
Log in

A New Approach to Network Optimization Using Chaos-Genetic Algorithm

  • Chapter
Computational Optimization and Applications in Engineering and Industry

Part of the book series: Studies in Computational Intelligence ((SCI,volume 359))

Abstract

Genetic Algorithms (GAs) have been widely used to solve network optimization problems with varying degrees of success. Part of the problem with GAs lies in the premature convergence when dealing with large-scale and complex problems; Caught in local optima, the algorithm might fail to reach the global optimum even after a large number of iterations. In order to overcome the problems with traditional GAs, a method is proposed to integrate Chaos Optimization Algorithms (COAs) with GA to fully exploit their respective searching advantages. The basic idea of COA is to transform the problem variables, by way of a map, from the solution space to a chaos space and to perform a search that benefits from the randomness, orderliness and ergodicity of chaos variable. In this chapter, we will first discuss network optimization in general, and then focus on how chaos theory can be incorporated into the GA in order to enhance its optimization capacities. We will also examine the efficiency of the proposed Chaos-Genetic algorithm in the context of two different types of network optimization problems, Grid scheduling and Network-on-Chip mapping problem.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Bondy, J.A., Murty, U.S.R.: Graph Theory. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  2. Korte, B., Vygen, J.: Combinatorial Optimization: Theory and Algorithms, Algorithms and Combinatorics, 4th edn. Springer, Heidelberg (2008)

    Google Scholar 

  3. Weise, T.: Global Optimization – Theory and Application, 2nd edn. Thomas Weise (2009)

    Google Scholar 

  4. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)

    Google Scholar 

  5. Yang, X.-S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Chichester (2010)

    Book  Google Scholar 

  6. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  7. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  8. Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, New York (2001)

    Google Scholar 

  9. Dorigo, M.: Optimization, Learning and Natural Algorithms (Phd Thesis), Politecnico di Milano, Italy (1992)

    Google Scholar 

  10. Glover, F., Laguna, M.: Tabu Search. USA Norwell. Kluwer Academic Publishers, Dordrecht (1997)

    Book  MATH  Google Scholar 

  11. Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)

    Article  Google Scholar 

  12. Ribeiro, C., Hansen, P.: Essays and Surveys in Metaheuristics. Kluwer Academic Publishers, Norwell (2002)

    MATH  Google Scholar 

  13. Melanie, M.: An Introduction to Genetic Algorithm, A Bradford book. A Bradford book MIT press, London (1998)

    Google Scholar 

  14. Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms, 2nd edn. Wiley-Interscience Publication, Hoboken (1998)

    MATH  Google Scholar 

  15. Stavroulakis, P.: Chaos Application in Telecommunications. CRC Press Taylor and Francis group, New York (2006)

    Google Scholar 

  16. Strogatz, S.: Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. In: Perseus Books (1994)

    Google Scholar 

  17. Tavazoei, M.S., Haeri, M.: Comparison of Different One-Dimensional Maps as Chaotic Search in Chaos Optimization Algorithms. Applied Mathematics and Computation 187(2), 1076–1085 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  18. He, Y.Y., Zhou, J.Z., Xiang, X.Q.: Comparison of different chaotic maps in particle swarm optimization algorithm for long term cascaded hydroelectric system scheduling. Chaos Solitons Fractals 42(5), 3169–3176 (2009)

    Article  MATH  Google Scholar 

  19. Ott, E.: Chaos in Dynamical Systems. Cambridge University Press, U.K (2002)

    MATH  Google Scholar 

  20. Erramili, A., Singh, R.P., Pruthi, P.: Modeling Packet Traffic with Chaotic Maps. Royal Institute of Technology, Sweden (1994), ISRN KTH/IT/R-94/18-SE

    Google Scholar 

  21. He, D., He, C., Jiang, L.G., Zhu, H.W., Hu, G.R.: A chaotic map with infinite collapses. In: Proc IEEE tencon., Kuala Lumpur, Malaysia, vol. 3(9), pp. 95–99 (2000)

    Google Scholar 

  22. He, D., He, C., Jiang, L.G., Zhu, H.W., Hu, G.: Chaotic characteristics of a one-dimensional iterative map with infinite collapses. IEEE Trans. 48(7), 900–906 (2001)

    MATH  MathSciNet  Google Scholar 

  23. Lu, Z., Shieh, L.S., Chen, G.R.: On robust control of uncertain chaotic systems: a sliding-mode synthesis via chaotic optimization. Chaos Solitons & Fractals 18(4), 819–836 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  24. Yang, J.J., Zhou, J.Z., Wu, W., Liu, F.: A chaos algorithm based on progressive optimality and tabu search algorithm. In: IEEE Proc. 4th International Conf. Machine Learning and Cybernetics, vol. 5, pp. 2977–2981 (2005)

    Google Scholar 

  25. Li, B., Jiang, W.S.: Chaos Optimization Method and Its Application. Control Theory and Application 14, 613–615 (1997)

    Google Scholar 

  26. Gao, L., Liu, X.: A Resilient Particle Swarm Optimization Algorithm based on chaos and applying it to optimize the fermentation process. International Journal of Information and Systems Sciences 5(3-4), 380–391 (2009)

    MathSciNet  Google Scholar 

  27. Bucolo, M., Caponetto, R., Fortune, L., Frasca, M., Rizzo, A.: Does chaos work better than noise? IEEE Circuits and Systems Magazine, 4–19 (2002)

    Google Scholar 

  28. Hongkai, W., Zhiming, C., Pingbo, W., Yinfeng, F.: Study of Intelligent Optimization Methods Applied in Fractional Fourier Transform. International Journal of Computer Theory and Engineering 2(4), 1793–8201 (2010)

    Google Scholar 

  29. Cheng, C.: Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resources Management 22(7), 895–909 (2008)

    Article  Google Scholar 

  30. Yan, X.F., Chen, D.Z., Hu, X.S.: Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model. Elsevier Computers and Chemical Engineering, 1390–1404 (2003)

    Google Scholar 

  31. Moein-Darbari, F., Khademzaheh, A., Gharoonifard, G.: CGMAP: A new Approach to Network-on-Chip Mapping Problem. IEICE Electronic Express 6(1), 27–34 (2009)

    Article  Google Scholar 

  32. Foster, I., Kesselman, C.: Computational Grids. In: The Grid: Blueprint for New Computing Infrastructure, pp. 15–52. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  33. Dong, F., Akl, S.G.: Scheduling Algorithms for Grid Computing: State of the Art and Open Problems. In: School of Computing, Queen’s University Kingston, Ontario, pp. 1–55 (2006)

    Google Scholar 

  34. Schopf, J.M.: Ten Actions When SuperScheduling, document of Scheduling Working Group. In: Global Grid Forum (2001)

    Google Scholar 

  35. Yang, Y., Casanova, H.: NP-complete Scheduling Problems. Journal of Computer and System Sciences 10, 434–439 (1975)

    Google Scholar 

  36. Mandal, A., Kennedy, K., Koelbel, C., Martin, G., Mellor-Crummey, J., Liu, B., Johnsson, L.: Scheduling Strategies for Mapping Application Workflows onto the Grid. In: IEEE International Symposium on High Performance Distributed Computing (HPDC 2005), Research Triangle Park, NC, pp. 125–134 (2005)

    Google Scholar 

  37. Eilam, T., Appleby, K., Breh, J., Breiter, G., Daur, H., Fakhouri, S.A., Hunt, G.D.H., Lu, T., Miller, S.D., Mummert, L.B., Pershing, J.A., Wagner, H.: Using a utility computing framework to develop utility systems. IBM System Journal 43(1), 97–120 (2004)

    Article  Google Scholar 

  38. Laszewski, G.V.: Java CoG Kit Workflow Concepts for Scientific Experiments. Argonne National Laboratory, Argonne, IL, USA Technique Report (2005)

    Google Scholar 

  39. Buyya, R., Giddy, J., Abramson, D.: An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications. In: 2nd Workshop on Active Middleware Services (AMS 2000). Kluwer Academic Press, Dordrecht (2000)

    Google Scholar 

  40. Sakellariou, R., Zhao, H., Tsiakkouri, E., Dikaiakos, M.: Scheduling workflows with budget constraints. In: Gorlatch, S., Danelutto, M. (eds.) Integrated Research in GRID Computing, ser., pp. 189–202. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  41. Zhu, Y.: A Survey on Grid Scheduling System. In: Department of Computer Science, Hong Kong University of Science and Technology (2003)

    Google Scholar 

  42. Gharooni-fard, G., Moein-darbari, F., Deldari, H., Morvaridi, A.: Scheduling of Scientific Workflows Using a Chaos-Genetic Algorithm. In: Procedia Computer Science, vol. 1(1), pp. 1439–1448 (2010)

    Google Scholar 

  43. Yu, J., Buyya, R.: Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms. Scientific Programming, 217–230 (2006)

    Google Scholar 

  44. Yu, J., Kirley, M., Buyya, R.: Multi-objective Planning for Workflow Execution on Grids. In: 8th IEEE ACM International Conference on Grid Computing, Singapore, pp. 10–17 (2007)

    Google Scholar 

  45. Blythe, J., Jain, S., Deelman, E., Gil, Y., Vahi, K., Mandal, A., Kennedy, K.: Task scheduling strategies for workflow-based applications in grids. In: 5th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2005), vol. 2, pp. 759–767 (2005)

    Google Scholar 

  46. Bjerregaard, T., Mahadevan, S.: A Survey of Research and Practices of Network-on-Chip. ACM Computing Surveys, New York (2006)

    Google Scholar 

  47. De Micheli, G., Benini, L.: Network on Chip: A New Paradigm for System-on-Chip Design., pp. 7–78 (2002)

    Google Scholar 

  48. Dally, W.J., Towles, B.: Route Packets, not Wires: on Chip Interconnection Networks. In: Proceedings of the Design Automation Conference (DAC), pp. 684–689 (2001)

    Google Scholar 

  49. Saastamoinen, I., Sigüenza-Tortosa, D., Nurmi, J.: Interconnect IP Node for Future System-on-Chip Designs. In: IEEE International workshop on Electronic design, Test, and Applications, New Zealand, pp. 116–120 (2002)

    Google Scholar 

  50. Sgroi, M., Sheets, M., Mihal, A., Keutzer, K., Malik, R.J., Sangiovanni-Vincentelli, A.: Addressing the System-on-Chip Interconnect Woes through Communication-based Design. In: The Design Automation Conference (DAC), pp. 667–672 (2001)

    Google Scholar 

  51. Lei, T., Kumar, S.: A Two Step Genetic Algorithm for Mapping Task Graphs to Network on Chip Architecture. In: Proceedings of the 3rd International Conference DSD 2003, Turkey, pp. 180–187 (2003)

    Google Scholar 

  52. Murali, S., Micheli, G.D.: Bandwidth-Constrained Mapping of Cores on to NoC Architectures. In: 4th International Conference on DATE 2004, pp. 896–901 (2004)

    Google Scholar 

  53. Shen, W.T., Chao, C.H., Lien, Y.K., Wu, A.Y.: A new Binomial Mapping and Optimization Algorithm for Reduced-Complexity Mesh-Based On-Chip Network. In: 1st IEEE International Symposium on Networks-on-Chip (NOCS 2007), New Jersey, pp. 317–322 (2007)

    Google Scholar 

  54. Moein-darbari, F., Khademzadeh, A., Gharooni-fard, G.: Evaluating the Performance of Chaos Genetic Algorithm for Solving the Network-on-Chip Mapping Problem. In: IEEE International Conference on Computational Science and Engineering, Vancouver, Canada, vol. 2, pp. 366–373 (2009)

    Google Scholar 

  55. Hu, J., Marculescu, R.: Energy-Aware Mapping for Tile-based NoC Architectures under Performance Constraints. ASP-DAC, 233–239 (2003)

    Google Scholar 

  56. Gharooni-fard, G., Khademzade, A., Moein-darbari, F.: Evaluating the Performance of Chaotic Maps in Network-on-Chip Mapping Problem. IEICE Electronic Express 6(12), 811–817 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Department of Islamic Azad University, Mashhad Branch, Young Researchers Club, Iran

    Golnar Gharooni-fard & Fahime Moein-darbari

Authors
  1. Golnar Gharooni-fard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fahime Moein-darbari
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Physical Laboratory, Mathematics and Scientific Computing, TW11 0LW, Teddington, Middlesex, UK

    Xin-She Yang

  2. Engineering Optimization & Modeling Center, School of Science and Engineering, Reykjavik University, Menntavegur 1, 101, Reykjavik, Iceland

    Slawomir Koziel

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Gharooni-fard, G., Moein-darbari, F. (2011). A New Approach to Network Optimization Using Chaos-Genetic Algorithm. In: Yang, XS., Koziel, S. (eds) Computational Optimization and Applications in Engineering and Industry. Studies in Computational Intelligence, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20986-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20986-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20985-7

  • Online ISBN: 978-3-642-20986-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation