Abstract
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.
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References
Aguirre, H., Tanaka, K.: Parallel Varying Mutation Genetic Algorithms. In: Proceedings of the Congress on Evolutionary Computation, Hawaii, USA (May 2002)
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)
Blickle, T., Thiele, L.: A Comparison of Selection Schemes used in Genetic Algorithms, Swiss Federal Institute of Technology. TIK-Report (1995)
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)
Buczak, L., Wang, H.: Optimization of Fitness Functions with Non-Ordered Parameters by Genetic Algorithms. In: Congress on Evolutionary Computation 2001, Korea, 5 (2001)
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)
De Jong, K.: Genetic algorithms are not function optimizers. In: Foundations of Genetic Algorithms, vol. 2, pp. 5–17. Morgan Kaufmann, San Mateo (1993)
Fogel, D.B.: Evolutionary Computation – Toward a New Philosophy of Machine Intelligence. IEEE Press, Los Alamitos (1995)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Jones, T.: Crossover, Macromutation, and Population-based Search. In: Proceedings of the Sixth International Conference on Genetic Algorithms, July 15-19 (1995)
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)
Li, B., Jiang, W.: A Novel Stochastic Optimization Algorithm. IEEE Transactions on System, Man, and Cybernetics, —Part B: Cybernetics 30(1) (February 2000)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)
Wang, H., Buczak, A., Wang, H.: A Novel Genetic Algorithm with King Strategy. ANNIE, St. Louis, USA, 11 (2003)
Wolpert, D.H., MacReady, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation (April 1996)
Schwefel, H.-P.: Evolution and Optimum Seeking. A Wiley-Interscience Publication. John Wiley & Sons, Inc. (1994)
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)
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Wang, H., Buczak, A.L., Jin, H., Wang, H., Li, B. (2004). Sensors Network Optimization by a Novel Genetic Algorithm. In: Jin, H., Gao, G.R., Xu, Z., Chen, H. (eds) Network and Parallel Computing. NPC 2004. Lecture Notes in Computer Science, vol 3222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30141-7_80
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DOI: https://doi.org/10.1007/978-3-540-30141-7_80
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