Reliability optimization for integrated radar systems

• Autorzy: Balicki J
• Języki publikacji: EN

Abstrakty

EN

In this paper, a problem of reliability optimization for a radar system is studied. A multi-criterion optimization problem has been formulated for finding a set of Pareto-optimal task assignments. Three patial criteria have been used for evaluation of task assignment: the reliability of system, the workload of a bottleneck computer, and the cost of computers. The computer resource constraints have been respected. An evolutionary algorithm with tabu mutation has been developed for finding Pareto-optimal task assignments. This approach deals with a modified genetic algorithm cooperating with the main evolutionary algorithm. An immune system activity is emulated by a modified genetic algorithm to handle constraints.

Słowa kluczowe

EN

sea surface safety; sea subsurface safety; distributed system; marine conference; radar system; evolutionary algorithm; conference; multicriteria optimization; Polish Navy

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2005
• Tom: 14
• Numer: Suppl.1
• Opis fizyczny: p.9-14,fig.,ref.

Twórcy

autor

Balicki J

• The Naval University of Gdynia, Smidowicza 69, 81-103 Gdynia, Poland

Bibliografia

• 1. BALICKI J., Model of the immune system to handle constraints in evolutionary algorithm for Pareto task assignments. in M. A. Kłopotek, S. T. Wierzchoń, K. Trojanowski (Eds.): Intelligent Information Processing and Web Mining, Springer-Verlag, Berlin Heidelberg. 3- 12, 2003.
• 2. BINH T. T., KORN U., Multiobjective Evolution Strategy for Constrained Optimization Problems, Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, Berlin, 357- 362, 1997.
• 3. COELLO COELLO C. A., CORTES N. C., Use of Emulations of the Immune System to Handle Constraints in Evolutionary Algorithms. Knowledge and Information Systems. An International Journal, 1, 1- 12, 2001.
• 4. COELLO COELLO C. A., VAN VELDHUIZEN D. A., LAMONT G. B., Evolutionary Algorithms for Solving Multi - Objective Problems. Kluwer Academic Publishers, New York, 2002.
• 5. DEB K., Multi - Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Chichester, 2001.
• 6. FORREST S., PERELSON A. S., Genetic Algorithms and the Immune System. Lecture Notes in Computer Science, 320- 325, 1991.
• 7. HELMAN P., FORREST S., An Efficient Algorithm for Generating Random Antibody Strings. Technical Report CS-94- 07, The University of New Mexico, Albuquerque, 1994.
• 8. JERNE N. K., The Immune System. Scientific American, 229 (1), 52- 60, 1973.
• 9. KAFIL M., AHMAD I., Optimal task assignment in heterogeneous distributed computing systems. IEEE Concurrency, 6, 42- 51, 1998.
• 10. KIM J., BENTLEY P. J., Immune Memory in the Dynamic Clonal Selection Algorithm. Proc. of the First Int. Conf. on Artificial Immune Systems, Canterbury, 57- 65, 2002.
• 11. KNOWLES J., CORNE D. W„ Approximating the Non-Dominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8 (2), 149- 172, 2002.
• 12. KOZIEL S., MICHALEWICZ Z., Evolutionary Algorithms, Homomorphous Mapping, and Constrained Parameter Optimisation. Evolutionary Computation, 7, 19- 44, 1999.
• 13. SMITH D., Towards a Model of Associative Recall in Immunological Memory. Technical Report 94- 9, University of New Mexico, Albuquerque, 1994.
• 14. SRINIVAS N, DEB K., Multiobjective Optimization Using Non-Dominated Sorting in Genetic Algorithms. Evolutionary Computation, 2 (3), 221- 248, 1994.
• 15. ULUNGU E. L., TEGHEM J., Multi - objective Combinatorial Optimization Problems: A Survey. Journal of Multi - Criteria Decision Analysis, 3, 83- 104, 1994.
• 16. WEGLARZ J. ed., Recent Advances in Project Scheduling. Kluwer Academic Publishers, Dordrecht, 1998.
• 17. WIERZCHON S. T., Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System. In M. Klopotek, M. Michalewicz and S. T. Wierzchon (eds.) Intelligent Information Systems. Springer Verlag, Heidelberg / New York 119- 133, 2000.
• 18. VAN VELDHUIZEN D. V., LAMONT G. B., Multiobjective Evolutionary Algorithms: Analyzing the State-Of-the-Art. Evolutionary Computation, 8 (2), 125- 147, 2000.
• 19. ZITZLER E., DEB K., THIELE L., Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8 (2), 173- 195, 2000.