A multi-objective HW-SW co-synthesis algorithm based on quantum-inspired evolutionary algorithm

International Journal of Computational Intelligence and Applications

Volumn 7, Issue 2, 2008, Pages 129-148

  Wei, Wenlong ^a   Li, Bin ^a   Zou, Yi ^a   Zhang, Wencong ^a   Zhuang, Zhenquan ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

HW SW co synthesis; Multi objective optimization; Multi PRI scheduling; Nondominated solutions; Pareto optimal; Quantum inspired evolutionary algorithm

Indexed keywords

ALGORITHMS; EMBEDDED SOFTWARE; INTEGRATED CIRCUITS; MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION; PARETO PRINCIPLE; SCHEDULING; SCHEDULING ALGORITHMS;

HW-SW CO-SYNTHESIS; MULTI-PRI SCHEDULING; NONDOMINATED SOLUTIONS; PARETO OPTIMAL; QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM;

EVOLUTIONARY ALGORITHMS;

EID: 59149087529     PISSN: 14690268     EISSN: None     Source Type: Journal    
DOI: 10.1142/S146902680800220X     Document Type: Article

References (22)

1. R. Ernest, Co-design of embedded systems: Status and trends, IEEE Des. Test Comput. (April-June 1998) 45-54.
2. R. K. Gupta and G. D. Micheli, System synthesis via hardware-software co-design, Stanford University: Computer Systems Laboratory, Technical Report: CSL-TR-92-548 (1992), p. 10.
3. Y. K. Kwok and I. Ahmad, Dynamic critical-path scheduling: An effective technique for allocating task graphs to multiprocessors, IEEE Trans. Parallel Distrib. Syst. 7 (1996) 506-521.
4. S. Prakash and A. C. Parker, Synthesis of application-specific heterogeneous multiprocessor systems, Parallel Distrib. Comput. 16 (1992) 338-351.
5. R. P. Dick and N. K. Jha, MOGAC: A multiobjective genetic algorithm for hardwaresoftware cosynthesis of distributed embedded systems, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 17 (1998) 920-935.
6. J. Hou and W. Wolf, Process partitioning for distributed embedded systems, in Proc. 4th Int. Workshop on Hardware/Software Co-Design eds. D. Thomas and R. Ernst (March 1996), pp. 18-20.
7. R. E. Steuer, Multiple Criteria Optimization: Theory, Computation, and Application (Wiley, New York, 1986).
8. J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, in Proc. 1st Int. Conf. Genetic Algorithms, eds. J. J. Grefenstette (Lawrence Erlbaum Associates, Hillsdale, 1985), pp. 93-100.
9. P. Hajela and C. Y. Lin, Genetic search strategies in multi-criterion optional design, Struct. Optim. 4 (1992) 99-107.
10. N. Srinivas and K. Deb, Multi-objective optimization using nondominated sorting in genetic algorithms, Evol. Comput. 2 (1994) 221-248.
11. K. Deb and D. E. Goldberg, An investigation of niche and species formation in genetic function optimization, in Proc. 3rd Int. Conf. Genetic Algorithms, eds. J. David (San Mateo, CA, Morgan Kaufmann, 1989), pp. 42-50.
12. J. D. Knowles and D. W. Corne, Approximating the nondominated front using the pareto archived evolution strategy, Evol. Comput. 8 (2000) 149-172.
13. T. Ray, K. Tai and C. Seow, An evolutionary algorithm for multi-objective optimization, Eng. Optim. 33 (2001) 399-424.
14. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA, IEEE Trans. Evol. Comput. 6 (2002) 182-197.
15. C. A. Coello and M. S. Lechuga, MOPSO: A proposal for multi-objective particle swarm optimization, in Proc. 2002 Congress on Evolutionary Computation, Vol. 2 (IEEE Press, Piscataway, 2002), pp. 1051-1056.
16. K. H. Han and J. H. Kim, Genetic quantum algorithm and its application to combinatorial optimization problem, in Proc. 2000 IEEE Congr. Evolutionary Computation (IEEE Press, Piscataway, 2000), pp. 1354-1360.
17. K. H. Han and J. H. Kim, Quantum-inspired evolutionary algorithm for a class of combinatorial optimization, IEEE Trans. Evol. Comput. 6 (2002) 580-593.
18. B. Li and Z. Q. Zhuang, Genetic algorithm based on the quantum probability representation, in Proc. 2002 Conf. Intelligent Data Engineering and Automatic Learning, eds. H. Yin et al., Lecture Notes in Computer Science, Vol. 2412 (2002), pp. 500-505.
19. B. Li et al., Quantum probability coding genetic algorithm and its applications, J. Electron. Inform. Techol. 27 (2005) 805-810 (in Chinese).
20. W. L. Wei, B. Li, Y. Zou and Z. Q. Zhuang, Multi-objective Q-bit coding genetic algorithm for hardware-software co-synthesis of embedded systems, in Proc. Sixth Int. Conf. Simulated Evolution and Learning (SEAL'06), eds. Wang et al., Lecture Notes in Computer Science, Vol. 4247 (Springer, October 2006), pp. 865-872.
21. L. F. Zhang, Research of HW-SW co-synthesis and virtual microprocessor, Ph.D dissertation, National University of Defense Technology, Changsha, China (2002).
22. Z. Eckart, K. Deb and L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8 (2000) 173-195.