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Cluster Computing
Published in: Cluster Computing 1/2017

26-12-2016

Performance optimization algorithm of radar signal processing system

Authors: Xiang Li, Jinsong Du

Published in: Cluster Computing | Issue 1/2017

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Abstract

A performance optimization algorithm of radar signal processing system is proposed. Based on a new modeling mode of radar signal processing system, the values of performance indexes of radar system can be predicted by a performance prediction method during the design phase. Then based on immune clone and immune memory theory in artificial immune system, this paper presents an immune memory clone constrained multi-objective optimization algorithm to generate the optimal system design schemes automatically. Experimental results show that the proposed performance optimization algorithm can search out the optimal design schemes that make system performance reach or near to optimum. The optimal solutions of the proposed algorithm are more diverse and better in terms of uniformity than NSGA-II and SPEA2.
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Literature
1.
Pareto, V.: Course Economic Politique. Vol. I and II. Rouge, Lausanne (1896)
2.
Fonseca, C.M., Fleming, P.J.: Genetic algorithm for multiobjective optimization: Formulation, discussion and generation. In: Proceedings of 5th International Conference on Genetic Algorithms, pp. 416–423 (1993)
3.
Srinivas, N., Deb, K.: Multiobjective optimization using non-dominated sorting in genetic algorithms. Evolut. Comput. 2(3), 221–248 (1994)CrossRef
4.
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of 1st IEEE Congress on Evolutionary Computation, pp. 82–87 (1994)
5.
Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evolut. Comput. 3(4), 257–271 (1999)CrossRef
6.
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. In: Proceedings of International Conference on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100 (2002)
7.
Corne, D.W., Knowles, J.D., Oates, M.J.: The Pareto-envelope based selection algorithm for multi-objective optimization. In: Proceedings of 6th International Conference on Parallel Problem Solving from Nature, pp. 869–878 (2000)
8.
Corne, D.W., Jerram, N.R., J D Knowles, et al, PESA-II: Region-based selection in evolutionary multi-objective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 283–290 (2001)
9.
Deb, K., Pratab, A., Agarwal, S., et al.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)CrossRef
10.
Coello, C.A., Cruz, C.N.: An approach to solve multiobjective optimization problems based on an artificial immune system. In: Proceedings of 1st International Conference on Artificial Immune System, pp. 212–221 (2002)
11.
12.
Freschi, F., Repetto, M.: Multiobjective optimization by a modified artificial immune system algorithm. In: Proceedings of 4th International Conference on Artificial Immune System, pp. 248–261 (2005)
13.
Freschi, F.: Multi-objective Artificial Immune Systems for Optimization in Electrical Engineering. Politecnico di Torino, Torino (2006)MATH
14.
Freschi, F., Repetto, M.: VIS: an artificial immune network for multi-objective optimization. Eng. Optim. 38(8), 975–996 (2006)CrossRef
15.
Yang, D.D., Jiao, L.C., Gong, M.G.: Adaptive multi-objective optimization based on nondominated solutions. Comput. Intell. 25(2), 84–108 (2009)MathSciNetCrossRef
16.
Ma, Y.Y.: Research and Application on Multi-agent Distributed Constraint Optimization Problem. Yangzhou University, Yangzhou (2015)
17.
Xiang, L.: Research of Artificial Immune Multi-agent Multi-objective Optimization Algorithm and Its Application. Ningbo University, Ningbo (2014)
18.
Ye, Y.J., Hu, Z.Y., Chen, Z.M.: Study on improved multi-objective quantum-inspired immune clone algorithm. J. Chongqing Univ. Technol. 12, 105–111 (2015)
19.
Qi, Y.T., Liu, F., Liu, J.L., et al.: Hybrid immune algorithm with EDA for multi-objective optimization. J. Softw. 24(10), 2251–2266 (2013)MathSciNetCrossRefMATH
20.
Lin, H., Peng, Y.: Immune clonal algorithm with fitness sharing for multi-objective optimization. Control Theory Appl. 28(2), 206–214 (2011)
21.
Qi, Y.T., Liu, F., Ren, Y., et al.: A cooperative immune coevolutionary algorithm for multi-objective optimization. Acta Electron. Sin. 42(5), 858–867 (2014)
22.
Xu, B.: Research and Application of Multi-objective Optimization Algorithms Base on Differential Evolution. East China University of Science and Technology, Shanghai (2013)
23.
Geng, H.T., Sun, J.Q., Jia, T.T.: A mixture crossover dynamic constrained multi-objective evolutionary algorithm based on self-adaptive start-up strategy. Pattern Recogn. Artif. Intell. 28(5), 411–421 (2015)
24.
Chen, Y.B., Li, J., Zhang, Q.: Analysis of using quantum evolutionary algorithm to solve constrained multi-objective optimization problem. Electron. Technol. Softw. Eng. 16, 185–187 (2015)
25.
Bi, X.J., Liu, G.A.: A cloud differential evolutionary algorithm for constrained multi-objective optimization. J. Harbin Eng. Univ. 33(8), 1022–1031 (2012)MathSciNet
26.
Bi, X.J., Zhang, L., Xiao, J.: Constrained multi-objective optimization algorithm based on dual populations. J. Comput. Res. Dev. 52(12), 2813–2823 (2015)
27.
Axel, H., Mario, D.C.: Performance and dependability evaluation of scalable massively parallel computer systems with conjoint simulation. ACM Trans. Model. Comput. Simul. (TOMACS) 8(4), 333–373 (1998)CrossRefMATH
28.
Khan, F.N., Khan, S.: A multi-DSP based simulator for architecture and high density algorithm exploration. In: 9th International Multitopic Conference. IEEE INMIC (2005)
29.
Li, X., Du, J.S., Liu, Y.Y., et al.: Design and realization of universal radar signal processing software. In: 2012 International Conference on Mechatronics and Control Engineering, pp. 1137–1142 (2012)
30.
Li, X., Du, J.S., Hu, J.T., et al.: Parallel signal processing software. In: 2014 International Conference on Multimedia, Communication and Computing Application, pp. 127–131 (2014)
31.
Jiao, L.C., Shang, R.H., Ma, W.P., et al.: Theory and Application of Multi-objective Optimization Immune Algorithm. Science Press, Beijing (2010)
Metadata
Title
Performance optimization algorithm of radar signal processing system
Authors
Xiang Li
Jinsong Du
Publication date
26-12-2016
Publisher
Springer US
Published in
Cluster Computing / Issue 1/2017
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-016-0710-6

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