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Erschienen in:
Membrane Computing - Key Concepts and Definitions Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

4. Engineering Optimization with Membrane Algorithms

verfasst von : Gexiang Zhang, Mario J. Pérez-Jiménez, Marian Gheorghe

Erschienen in: Real-life Applications with Membrane Computing

Verlag: Springer International Publishing

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Abstract

In this chapter are described engineering applications of the membrane algorithms introduced in Chap. 3. The engineering problems we consider are the following: radar emitter signal analysis, digital image processing, controller design, mobile robot path planning, constrained manufacturing parameter optimization problems, distribution network reconfiguration and electric power system fault diagnosis.

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Vorheriges Kapitel Membrane Algorithms
Nächstes Kapitel Electric Power System Fault Diagnosis with Membrane Systems
Literatur
1.
Abdelaziz, A., F. Mohammed, S. Mekhamer, and M. Badr. 2009. Distribution systems reconfiguration using a modified particle swarm optimization algorithm. Electric Power Systems Research 79 (11): 1521–1530.CrossRef
2.
Akay, B., and D. Karaboga. 2012. Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing 23 (4): 1001–1014.CrossRef
3.
Bergeau, F., and S. Mallat. 1994. Matching pursuit of Images. In Proceedings of IEEE International Conference on Signal Processing, 330–333.
4.
Cardoso, G., J.G. Rolim, and H.H. Zurn. 2008. Identifying the primary fault section after contingencies in bulk power systems. IEEE Transactions on Power Delivery 23 (3): 1335–1342.CrossRef
5.
Chang, C.S., J.M. Chen, D. Srinivasan, F.S. Wen, and A.C. Liew. 1997. Fuzzy logic approach in power system fault section identification. IEEE Proceedings–Part C, Generation, Transmission and Distribution 144 (5): 406–414.CrossRef
6.
Chen, J.W. 2008. Optimal design of control system based on membrane computing optimization method, Master dissertation, Zhejiang University, Hangzhou.
7.
Cheng, J., G. Zhang, and X. Zeng. 2011. A novel membrane algorithm based on differential evolution for numerical optimization. International Journal of Unconventional Computing 7 (3): 159–183.
8.
Davis, G., S. Mallat, and M. Avellaneda. 1997. Adaptive greedy approximation. Journal of Constructive Approximation 13 (1): 57–98.MathSciNetCrossRefMATH
9.
Gao, H., and J. Cao. 2012. Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation. Journal of Systems Engineering and Electronics 23 (5): 679–688.CrossRef
10.
Gao, H., J. Cao, and Y. Zhao. 2012. Membrane quantum particle swarm optimisation for cognitive radio spectrum allocation. International Journal of Computer Applications in Technology 43 (4): 359–365.CrossRef
11.
Gao, H., G.H. Xu, and Z.R. Wang. 2006. A novel quantum evolutionary algorithm and its application. In Proceedings of the Sixth World Congress on Intelligent Control and Automation, 3638–3642.
12.
Ghorbani, M.A., S.H. Hosseinian, and B. Vahidi. 2008. Application of ant colony system algorithm to distribution networks reconfiguration for loss reduction. In Proceedings of International Conference on Optimization of Electrical and Electronic Equipment, 269–273.
13.
Han, K.H., and J.H. Kim. 2000. Genetic quantum algorithm and its application to combinatorial optimization problem. In Proceedings of IEEE Congress on Evolutionary Computation, 1354–1360.
14.
Han, K.H., and J.H. Kim. 2002. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation 6 (6): 580–593.MathSciNetCrossRef
15.
He, Q., and L. Wang. 2007. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence 20 (1): 89–99.CrossRef
16.
Huang, S.J., and X.Z. Liu. 2013. Application of artificial bee colony-based optimization for fault section estimation in power systems. International Journal of Electrical Power & Energy Systems 44 (1): 210–218.CrossRef
17.
Huang, F.Z., L. Wang, and Q. He. 2007. An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation 186 (1): 340–356.MathSciNetCrossRefMATH
18.
Jeon, Y.J., and J.C. Kim. 2004. Application of simulated annealing and tabu search for loss minimization in distribution systems. International Journal of Electrical Power & Energy Systems 26 (1): 9–18.CrossRef
19.
Lee, H.J., B.S. Ahn, and Y.M. Park. 2000. A fault diagnosis expert system for distribution substations. IEEE Transactions on Power Delivery 15 (1): 92–97.
20.
Li, Z.K., X.Y. Chen, K. Yu, Y. Sun, and H.M. Liu. 2008. A hybrid particle swarm optimization approach for distribution network reconfiguration problem. In Proceedings of Power and Energy Society General Meeting, 1–7.
21.
Liao, T.W. 2010. Two hybrid differential evolution algorithms for engineering design optimization. Applied Soft Computing 10 (4): 1188–1199.CrossRef
22.
Lin, X.N., S.H. Ke, Z.T. Li, H.L. Weng, and X.H. Han. 2010. A fault diagnosis method of power systems based on improved objective function and genetic algorithm-tabu search. IEEE Transactions on Power Delivery 25 (3): 1268–1274.CrossRef
23.
Liu, J.K. 2004. Advanced PID control and Matlab simulation, 2nd ed. Beijing: PHEI Press.
24.
Liu, C., G. Zhang, X. Zhang, and H. Liu. 2009. A memetic algorithm based on P systems for IIR digital filter design. In Proceedings of the Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, 330–334.
25.
Liu, C., G. Zhang, Y. Zhu, C. Fang, and H. Liu. 2009. A quantum-inspired evolutionary algorithm based on P systems for radar emitter signals. In Proceedings of the 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 24–28.
26.
Liu, H., Z. Cai, and Y. Wang. 2010. Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing 10 (2): 629–640.CrossRef
27.
Liu, C., G. Zhang, H. Liu, M. Gheorghe, and F. Ipate. 2010. An improved membrane algorithm for solving time-frequency atom decomposition. In Membrane Computing. WMC 2009, vol. 5957, ed. G. Păun, M.J. Pérez-Jiménez, A. Riscos-Núñez, G. Rozenberg, and A. Salomaa, 371–384. Lecture Notes in Computer Science. Berlin: Springer.
28.
Mekhamer, S., A. Abdelaziz, F. Mohammed, and M. Badr. 2008. A new intelligent optimization technique for distribution systems reconfiguration. In Proceedings of International Middle-East Power System Conference, 397–401.
29.
Mezura-Montes, E., and C.A.C. Coello. 2005. Useful infeasible solutions in engineering optimization with evolutionary algorithms. In MICAI 2005: Advances in Artificial Intelligence, vol. 3789, ed. A. Gelbukh, A. de Albornoz, and H. Terashima-Marín, 652–662. Lecture Notes in Artificial Intelligence. Berlin: Springer.
30.
Martín, J.A., and A.J. Gil. 2008. A new heuristic approach for distribution systems loss reduction. Electric Power Systems Research 78 (11): 1953–1958.CrossRef
31.
Masehian, E., and D. Sedighizadeh. 2007. Classic and heuristic approaches in robot motion planning-a chronological review. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering 1 (5): 228–233.
32.
Mallat, S.G., and Z.F. Zhang. 1993. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41 (12): 3397–3415.CrossRefMATH
33.
Nournejad, F., R. Kazemzade, and A.S. Yazdankhah. 2011. A multiobjective evolutionary algorithm for distribution system reconfiguration. In Proceedings of the 16th Conference on Electrical Power Distribution Networks, 1–7.
34.
Pierre, V., and F. Pascal. 2001. Efficient image representation by anisotropic refinement in matching pursuit. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1757–1760.
35.
Parsopoulos, K.E., and M.N. Vrahatis. 2005. Unified particle swarm optimization for solving constrained engineering optimization problems. In Advances in Natural Computation (ICNC 2005), vol. 3612, ed. L. Wang, K. Chen, and Y.S. Ong, 582–591. Lecture Notes in Computer Science. Berlin: Springer.
36.
Rao, R.V., V.J. Savsani, and D.P. Vakharia. 2011. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43 (3): 303–315.CrossRef
37.
Swarnkar, A., N. Gupta, and K. Niazi. 2011. Efficient reconfiguration of distribution systems using ant colony optimization adapted by graph theory. In Proceedings of Power and Energy Society General Meeting, 1–8.
38.
Sun, J., S.Y. Qin, and Y.H. Song. 2004. Fault diagnosis of electric power systems based on fuzzy Petri nets. IEEE Transactions on Power Systems 19 (4): 2053–2059.CrossRef
39.
Tuncer, A., and M. Yildirim. 2012. Dynamic path planning of mobile robots with improved genetic algorithm. Computers & Electrical Engineering 38 (6): 1564–1572.CrossRef
40.
Wang, C.X., A.J. Zhao, H. Dong, and Z.J. Li. 2009. An improved immune genetic algorithm for distribution network reconfiguration. In Proceedings of International Conference on Information Management, Innovation Management and Industrial Engineering, 218–223.
41.
Wang, T., J. Wang, H. Peng, and M. Tu. 2012. Optimization of PID controller parameters based on PSOPS algorithm. ICIC Express Letters 6 (1): 273–280.
42.
Wang, T., G.X. Zhang, J.B. Zhao, Z.Y. He, J. Wang, and M.J. Pérez-Jiménez. 2015. Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Transactions on Power Systems 30 (3): 1182–1194.CrossRef
43.
Wang, X., G. Zhang, J. Zhao, H. Rong, F. Ipate, and R. Lefticaru. 2015. A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning. International Journal of Computers, Communications and Control 10 (5): 732–745.CrossRef
44.
Wen, F.S., and Z.X. Han. 1995. Fault section estimation in power systems using a genetic algorithm. Electric Power Systems Research 34 (3): 165–172.CrossRef
45.
Yang, S., and N. Wang. 2012. A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model. International Journal of Hydrogen Energy 37 (10): 8465–8476.CrossRef
46.
Zhang, G., J. Cheng, M. Gheorghe, and Q. Meng. 2013. A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing 13 (3): 1528–1542.CrossRef
47.
Zhang, G., J. Cheng, and M. Gheorghe. 2014. Dynamic behavior analysis of membrane-inspired evolutionary algorithms. International Journal of Computers, Communications and Control 9 (2): 235–250.CrossRef
48.
Zhang, G., M. Gheorghe, and Y. Li. 2012. A membrane algorithm with quantum-inspired subalgorithms and its application to image processing. Natural Computing 11 (4): 701–717.MathSciNetCrossRefMATH
49.
Zhang, G., M. Gheorghe, L. Pan, and M.J. Pérez-Jiménez. 2014. Evolutionary membrane computing: a comprehensive survey and new results. Information Sciences 279: 528–551.CrossRef
50.
Zhang, G., M. Gheorghe, and C. Wu. 2008. A quantum-inspired evolutionary algorithm based on P systems for knapsack problem. Fundamenta Informaticae 87 (1): 93–116.MathSciNetMATH
51.
Zhu, Y.L., L.M. Huo, and J.L. Liu. 2006. Bayesian networks based approach for power systems fault diagnosis. IEEE Transactions on Power Delivery 21 (2): 634–639.CrossRef
52.
Zhang, G., N. Li, W. Jin, and L. Hu. 2006. Novel quantum genetic algorithm and its application. Frontiers of Electrical and Electronic Engineering in China 1 (1): 31–36.CrossRef
53.
Zhang, G., C. Liu, and H. Rong. 2010. Analyzing radar emitter signals with membrane algorithms. Mathematical and Computer Modelling 52 (11–12): 1997–2010.CrossRef
54.
Zhang, G., and H. Rong. 2007. Real-observation quantum-inspired evolutionary algorithm for a class of numerical optimization problems. In Computational Science-ICCS 2007, vol. 4490, ed. Y. Shi, G.D. van Albada, J. Dongarra, and P.M.A. Sloot, 989–996. Lecture Notes in Computer Science. Berlin: Springer.
55.
Zhang, G.X., H.N. Rong, F. Neri, and M.J. Pérez-Jiménez. 2014. An optimization spiking neural P system for approximately solving combinatorial optimization problems. International Journal Neural Systems, 24 (5), Article no. 1440006, 16 p.
56.
Zhao, J., and N. Wang. 2011. A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling. Computers and Chemical Engineering 35 (2): 272–283.CrossRef
57.
Zhang, G., F. Zhou, X. Huang, J. Cheng, M. Gheorghe, F. Ipate, and R. Lefticaru. 2012. A novel membrane algorithm based on particle swarm optimization for solving broadcasting problems. Journal of Universal Computer Science 13 (18): 1821–1841.MATH
58.
Zhang, H., G. Zhang, H. Rong, and J. Cheng. 2010. Comparisons of quantum rotation gates in quantum-inspired evolutionary algorithms. In Proceedings of the 6th International Conference on Natural Computation, 2306–2310.
59.
Zhou, F., G. Zhang, H. Rong, M. Gheorghe, J. Cheng, F. Ipate, and R. Lefticaru. 2010. A particle swarm optimization based on P systems. In Proceedings of the 6th International Conference on Natural Computation, 3003–3007.
Metadaten
Titel
Engineering Optimization with Membrane Algorithms
verfasst von
Gexiang Zhang
Mario J. Pérez-Jiménez
Marian Gheorghe
Copyright-Jahr
2017
Buch
Real-life Applications with Membrane Computing

Print ISBN: 978-3-319-55987-2

Electronic ISBN: 978-3-319-55989-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-55989-6_4