Sensors Network Optimization by a Novel Genetic Algorithm

  • Hui Wang
  • Anna L. Buczak
  • Hong Jin
  • Hongan Wang
  • Baosen Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3222)


This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best accuracy of location of desired objects. The system constructs a GA with the appropriate internal structure for the optimization problem at hand, and King Mutation C2 finds the quasi-optimal combination of sensors that can detect and/or locate the objects. The study is performed for the sensor network optimization problem with five objects to detect/track and the results obtained by a canonical GA and King Mutation C2 are compared.


Genetic Algorithm Sensor Network Object Tracking Reproduction Process Network Objective 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aguirre, H., Tanaka, K.: Parallel Varying Mutation Genetic Algorithms. In: Proceedings of the Congress on Evolutionary Computation, Hawaii, USA (May 2002)Google Scholar
  2. 2.
    Areibi, S.: An Integrated Genetic Algorithm With Dynamic Hill Climbing for VLSI Circuit Partitioning. In: Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, Nevada, July 2000, IEEE, Los Alamitos (2000)Google Scholar
  3. 3.
    Blickle, T., Thiele, L.: A Comparison of Selection Schemes used in Genetic Algorithms, Swiss Federal Institute of Technology. TIK-Report (1995)Google Scholar
  4. 4.
    Buczak, L., Wang, H., Darabi, H., Jafari, M.A.: Genetic Algorithm Convergence Study for Sensor Network Optimization. Information Sciences 133(3-4), 267–282 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Buczak, L., Wang, H.: Optimization of Fitness Functions with Non-Ordered Parameters by Genetic Algorithms. In: Congress on Evolutionary Computation 2001, Korea, 5 (2001)Google Scholar
  6. 6.
    Deb, K., Agrawal, S.: Understanding Interactions Among Genetic Algorithm Parameters. In: Banzhaf, W., Reeves, C. (eds.) Foundations of Genetic Algorithms, vol. 5, Morgan Kaufmann Publishers, Inc., San Francisco (1999)Google Scholar
  7. 7.
    De Jong, K.: Genetic algorithms are not function optimizers. In: Foundations of Genetic Algorithms, vol. 2, pp. 5–17. Morgan Kaufmann, San Mateo (1993)Google Scholar
  8. 8.
    Fogel, D.B.: Evolutionary Computation – Toward a New Philosophy of Machine Intelligence. IEEE Press, Los Alamitos (1995)zbMATHGoogle Scholar
  9. 9.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  10. 10.
    Jones, T.: Crossover, Macromutation, and Population-based Search. In: Proceedings of the Sixth International Conference on Genetic Algorithms, July 15-19 (1995)Google Scholar
  11. 11.
    Kadar, I.: Optimum Geometry Selection For Sensor Fusion. In: Kadar, I. (ed.) Signal Processing, Sensor Fusion and Target Recognition VII. SPIE, vol. 3374, pp. 13–15. The International Society for Optical Engineering, Bellingham (1998)CrossRefGoogle Scholar
  12. 12.
    Li, B., Jiang, W.: A Novel Stochastic Optimization Algorithm. IEEE Transactions on System, Man, and Cybernetics, —Part B: Cybernetics 30(1) (February 2000)Google Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)CrossRefzbMATHGoogle Scholar
  14. 14.
    Wang, H., Buczak, A., Wang, H.: A Novel Genetic Algorithm with King Strategy. ANNIE, St. Louis, USA, 11 (2003)Google Scholar
  15. 15.
    Wolpert, D.H., MacReady, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation (April 1996)Google Scholar
  16. 16.
    Schwefel, H.-P.: Evolution and Optimum Seeking. A Wiley-Interscience Publication. John Wiley & Sons, Inc. (1994)Google Scholar
  17. 17.
    Srinivas, M., Patnaik, L.M.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics 24(4), 656–667 (1994)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2004

Authors and Affiliations

  • Hui Wang
    • 1
  • Anna L. Buczak
    • 2
  • Hong Jin
    • 1
  • Hongan Wang
    • 1
  • Baosen Li
    • 3
  1. 1.Institute of SoftwareChinese Academy of SciencesChina
  2. 2.Lockheed Martin Advanced Technology LaboratoriesCherry HillUSA
  3. 3.Zibo Electric Power CompnayZibo.ShandongChina

Personalised recommendations