Quantum Evolutionary Cellular Automata Mapping Optimization Technique Targeting Regular Network on Chip

  • Belkebir DjalilaEmail author
  • Boutekkouk FatehEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)


This paper presents a novel method for solving the mapping and scheduling problems in network on chip based on quantum evolutionary cellular automata (QECA). The method applies QECA to handle the multimedia application IP placement and scheduling problem. The QECA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. Thus, the mechanism of the QECA method can inherently treat the balance between exploration and exploitation where each Q-bit individual can represent and explore all possible states and drive it to exploit a single state. The use of quantum bit representation leads to better population diversity compared with the classical bit representations while the use of quantum gate drive the population towards the best solution. The achieved results are about 0.99 % of the fitness function over 110 generations.


Quantum genetic algorithm Cellular automata Network on chip Quantum computing Energy consumption 


  1. 1.
    Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6, 580–593 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Narayanan, A., Moore,M.: Quantum-inspired genetic algorithms. In: Evolutionary Computation, Proceedings of IEEE International Conference on IEEE, 1996, pp. 61–66 (1996)Google Scholar
  3. 3.
    Yang, J., Li, B.: Research of quantum genetic algorithm and its application in blind source separation. J. Electron. 20(1), 62–68 (2003)Google Scholar
  4. 4.
    Wong, S.C., Winbond TSM: An Extraction Method to Determine Interconnect Parasitic Parameters. Feb. 2000Google Scholar
  5. 5.
    Ho, R.: On-chip wires: scaling and efficiency. On Aug. 2003Google Scholar
  6. 6.
    Laboudi, Z., Chikhi, S.: Comparison of genetic algorithm and quantum genetic algorithm. Int. Arab J. Inf. Technol. 9, 243 (2012)Google Scholar
  7. 7.
    Bhat, S.: Energy models for network-on-chip components. On Dec 2005Google Scholar
  8. 8.
    Predictive Technology Model (PTM), Arizona State University, Available:, Last accessed: Dec. 2010

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Authors and Affiliations

  1. 1.Research Laboratory on Computer Science’s Complex System (RELA(CS)2)Oum El-Bouaghi UniversityOum El-BouaghiAlgeria

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