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Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

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Abstract

In this paper we present three meta-heuristic approaches for FPGA segmented channel routing problems (FSCRPs) with a new cost function in which the cost of each assignment is not known in advance, and the cost of a solution only can be obtained from entire feasible assignments. Previous approaches to FSCPs cannot be applied to this kind of cost functions, and meta-heuristics are a good option to tackle the problem. We present two hybrid algorithms which use a Hopfield neural network to solve the problem's constraints, mixed with a Genetic Algorithm (GA) and a Simulated Annealing (SA). The third approach is a GA which manages the problem's constraints with a penalty function. We provide a complete analysis of the three metaheuristics, by tested them in several FSCRP instances, and comparing their performance and suitability to solve the FSCRP.

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References

  1. F. N. Abuali, D. A. Schoenefeld, and R. L. Wainwright, “Terminal assignment in a communications network using genetic algorithms,” in Proc. 22sd Annual ACM Computer Science Conference, J. Werth and L. Werth (eds.), ACM press, 1994, pp. 74–81.

  2. J. Balicki, A. Stateczny, and B. Zak, “Genetic algorithms and Hopfield neural networks for solving combinatorial problems,” Appl. Math. and Comp. Sci., vol. 7, no. 3, pp. 568–592, 1997.

    MathSciNet  Google Scholar 

  3. C. Bousoño-Calzón and A. R. Figueiras-Vidal, “Emerging techniques for dynamic frequency assignment: merging genetic algorithms and neural networks,” in Proc. of Information Systems Technology Symposium, pp. 12.1–12.5, Aalborg, Denmark, 1998.

  4. S. Burman, C. Kamalanathan, and N. Sherwani, “New channel segmentation model and associated routing algorithm for high performance FPGA's,” in Proc. IEEE Int. Conf. Computer Aided Design, L. Trevillyan and M. Lightner (eds.), 1992, pp. 22–25.

  5. C. Calderón-Macías, M. K. Sen, and P. L. Stoffa, “Hopfield neural networks, and mean field annealing for seismic deconvolution and multiple attenuation,” Geophysics, vol. 62, no. 3, pp. 992–1002, 1997.

    Google Scholar 

  6. N. Funabiki, M. Yoda, J. Kitamichi, and S. Nishikawa, “A gradual neural network approach for FPGA segmented channel routing problems,” IEEE Trans. Systems, Man and Cybern., Part B, vol. 29, pp. 481–489, 1999.

    Google Scholar 

  7. A. E. Gamal, J. Greene, J. Reyneri, E. Rogoyski, K. A. El-Ayat, and A. Mohsen, “An architecture for electrically configurable gate arrays,” IEEE J. Solid-State Circuits, vol. 24, pp. 394-398, 1989.

    Google Scholar 

  8. A. E. Gamal, J. Greene, and V. Roychowdhury, “Segmented channel routing is nearly as efficient as channel routing (and just as hard),” in Proc. 13th Conf. Advanced Reserch VLSI, C. H. Séquin (ed.), 1991, pp. 192–211.

  9. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley: Reading, MA, 1989.

    Google Scholar 

  10. J. González, I. Rojas, H. Pomares, M. Salmerón, and J. J. Merelo, “Web newspaper layout optimization using simulated annealing,” IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 32, no. 5, pp. 686–691, 2002.

    Google Scholar 

  11. J. W. Greene, V. Roychowdhury, S. Kaptanoglu, and A. E. Gamal, “Segmented channel routing problems,” in Proc. 27th Design Automat. Conf., R. C. Smith (ed.), 1990, pp. 567–572.

  12. S. Khuri and T. Chiu, “Heuristic algorithms for the terminal assignment problem,” in Proc. of the 1997 ACM Symposium on Applied Computing, B. Bryant, J. Carroll, J. Hightower and K. M. George (eds.), ACM press, 1997, pp. 247–251.

  13. H. Kim, Y. Hayashi, and K. Nara, “An algorithm for thermal unit maintenance scheduling through combined use of GA, SA and TS,” IEEE Trans. Power Systems, Vol. 12, no. 1, pp. 329–335, 1997.

    Article  Google Scholar 

  14. S. Kirpatrick, “Optimization by simulated annealing–Quantitative studies,” J. Stat. Phys., vol. 34, pp. 975–986, 1984.

    Google Scholar 

  15. S. Kirpatrick, C. D. Gerlatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680, 1983.

    MathSciNet  Google Scholar 

  16. W. N. Li, “The complexity of the segmented channel routing,” IEEE Trans. Computer-Aided Desing, vol. 14, pp. 518–523, 1993.

    Google Scholar 

  17. J. Richardson, M. Palmer, G. Liepins, and M. Hilliard, “Some guidelines for genetic algorithms with penalty functions,” in Proc. 3rd Int. Conf. Genetic Algorithms and Applications, J. D. Schaffer (ed.), Morgan Kaufmann: California, 1989.

  18. K. Roy, “A bounded search algorithm for segmented channel routing for FPGA's and associated channel architecture issues,” IEEE Trans. Computer-Aided Desing, vol. 12, pp. 1695–1705, 1993.

    Google Scholar 

  19. V. Roychowdhury, J. W. Greene, and A. E. Gamal, “Segmented channel routing,” IEEE Trans. Computer-Aided Design, vol. 12, pp. 75–95, 1993.

    Article  Google Scholar 

  20. T. P. Runarsson and X. Yao, “Stochastic ranking for constrained evolutionary optimization,” IEEE Trans. Evol. Comput., vol. 3, no. 4, pp. 284–294, 2000.

    Google Scholar 

  21. S. Salcedo-Sanz, C. Bousoño-Calzón, and A. R. Figueiras-Vidal, “A mixed neural-genetic algorithm for the broadcast scheduling problem,” IEEE Trans. Wireless Commun., vol. 2, no. 2, pp. 277–283, 2003.

    Article  Google Scholar 

  22. S. Salcedo-Sanz, R. Santiago-Mozos, and C. Bousoño-Calzón, “A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems,” IEEE Trans. Syst. Man Cybern. B, vol. 34, no. 2, pp. 1108–1116, 2004.

    Article  Google Scholar 

  23. N. Sherwani, Algorithms for VLSI Physical Desing Automation, 2nd edition, Kluwer: Norwell, MA, 1995.

    Google Scholar 

  24. Y. Shrivastava, S. Dasgupta, and S. M. Reddy, “Guaranteed convergence in a class of Hopfield networks,” IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 951–961, 1992.

    Article  Google Scholar 

  25. K. Smith and M. Palaniswami, and M. Krishnamoorthy, “Neural techniques for combinatorial optimization with applications,” IEEE Trans. Neural Networks, vol. 9, no. 6, pp. 1301–1318, 1998.

    Article  Google Scholar 

  26. G. Wang and N. Ansari, “Optimal broadcast scheduling in packet radio networks using mean field annealing,” IEEE J. Select. Areas Commun., vol. 15, no. 2, pp. 250–259, 1997.

    Article  Google Scholar 

  27. Y. Watanabe, N. Mizuguchi, and Y. Fujii, “Solving optimization problems by using a Hopfield neural network and genetic algorithm combination”. Systems and Computers in Japan, vol. 29, no. 10, pp. 68–73, 1998.

    Article  Google Scholar 

  28. X. Yao, “Optimization by genetic annealing,” in Proc. of the 2nd Australian Conf. on Neural Networks, M. Jabri (Ed.) Sydney, 1991, pp. 94–97.

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Authors and Affiliations

  1. Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Spain

    Sancho Salcedo-Sanz

  2. School of Computer Science, The University of Birmingham, UK

    Yong Xu & Xin Yao

Authors
  1. Sancho Salcedo-Sanz
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  2. Yong Xu
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  3. Xin Yao
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Corresponding author

Correspondence to Sancho Salcedo-Sanz.

Additional information

This work has been partially supported by a research project of the Universidad de Alcalá, project number UAH PI2005/078.

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Salcedo-Sanz, S., Xu, Y. & Yao, X. Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions. Genet Program Evolvable Mach 6, 359–379 (2005). https://doi.org/10.1007/s10710-005-3295-z

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