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2017 | OriginalPaper | Buchkapitel

3. 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

Membrane Algorithms (MAs) area is focusing on developing new variants of meta-heuristic algorithms for solving complex optimization problems by using either the hierarchical or network membrane structures, evolution rules and computational capabilities of membrane systems and the methods and well-established techniques employed in Evolutionary Computation. MAs studied in this volume, and described in this Chapter, refer to four variants of meta-heuristics using the hierarchical structure of the membrane systems - nested membrane structure, one-level membrane structure, hybrid membrane structure and dynamic membrane structure; whereas those using the network structure consist of two subcategories - statical network structure and dynamical network structure.

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Vorheriges Kapitel Fundamentals of Evolutionary Computation

Nächstes Kapitel Engineering Optimization with Membrane Algorithms

Becerra, R.L., and C.A.C. Coello. 2006. Cultured differential evolution for constrained optimization. Computer Methods in Applied Mechanics and Engineering 195 (33–36): 4303–4322.MathSciNetMATHCrossRef

Bernardini, F., and M. Gheorghe. 2008. Population P systems. Journal of Universal Computer Science 10 (5): 509–539.MathSciNet

Burke, E., S. Gustafson, and G. Kendall. 2004. Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation 8 (1): 47–62.CrossRef

Chen, H., and J. Lu. 2012. A constrained optimization evolutionary algorithm based on membrane computing. Journal of Digital Information Management 10 (2): 121–125.

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.

Cheng, J., G. Zhang, and T. Wang. 2015. A membrane-inspired evolutionary algorithm based on population P systems and differential evolution for multi-objective optimization. Journal of Computational and Theoretical Nanoscience 12 (7): 1150–1160.CrossRef

Coello, C.A.C., and N.C. Cortés. 2004. Hybridizing a genetic algorithm with an artificial immune system for global optimization. Engineering Optimization 36 (5): 607–634.MathSciNetCrossRef

Coello, C.A.C., G.B. Lamont, and D.A.V. Veldhuizen. 2007. Evolutionary algorithms for solving multi-objective problems, 2nd ed. New York: Springer.MATH

Deb, K. 2000. An efficient constraint handling method for genetic algorithm. Computer Methods in Applied Mechanics and Engineering 186 (2–4): 311–338.MATHCrossRef

10.

Deb, K., M. Mohan, and S. Mishra. 2005. Evaluating the \(\epsilon \)-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evolutionary Computation 13 (4): 501–525.CrossRef

11.

Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2): 182–197.CrossRef

12.

Elias, S., V. Gokul, K. Krithivasan, M. Gheorghe, and G. Zhang. 2012. A variant of the distributed P system for real time cross layer optimization. Journal of Universal Computer Science 18 (13): 1760–1781.MathSciNetMATH

13.

Escuela, G., and M.A. Gutiérrez-Naranjo. 2010. An application of genetic algorithms to membrane computing. In Proceedings of the Eighth Brainstorming Week on Membrane Computing, 101–108.

14.

Folino, G., C. Pizzuti, and G. Spezzano. 2001. Parallel hybrid method for SAT that couples genetic algorithms and local search. IEEE Transactions on Evolutionary Computation 5 (4): 323–334.MATHCrossRef

15.

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

16.

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

17.

García, S., D. Molina, M. Lozano, and F. Herrera. 2009. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics 15: 617–644.MATHCrossRef

18.

Garey, M., and D. Johnson. 1979. Computers and intractability: a guide to the theory of NP-completeness. New York: W. H. Freeman & Co.MATH

19.

Glover, F., E. Taillard, and D. de Werra. 1993. A users guide to tabu search. Annals of Operations Research 41 (1): 3–28.MATHCrossRef

20.

Gottlieb, J., E. Marchiori, and C. Rossi. 2001. Evolutionary algorithms for the satisfiability problem. Evolutionary Computation 10 (1): 35–50.CrossRef

21.

Hajela, P., and J.S. Yoo. 1999. Immune network modelling in design optimization. In New Ideas in Optimization, ed. D. Corne, M. Dorigo, and F. Glover, 167–183. New York: McGraw-Hill.

22.

Han, K., and J. Kim. 2000. Genetic quantum algorithm and its application to combinatorial optimization problem. In Proceedings of IEEE Congress on Evolutionary Computation, 1354–1360.

23.

Han, K., and J. Kim. 2002. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation 6 (6): 580–593.MathSciNetCrossRef

24.

Han, K., and J. Kim. 2004. Quantum-inspired evolutionary algorithms with a new termination criterion, H\(_{\epsilon }\) gate, and two-phase scheme. IEEE Transactions on Evolutionary Computation 8 (2): 156–169.CrossRef

25.

Herrera, F., and M. Lozano. 1996. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In F. Herrera, J.L. Verdegay (eds.), Genetic Algorithms and Soft Computing, Physica-Verlag, pages 95–125,

26.

Huang, L., and I.H. Suh. 2009. Controller design for a marine diesel engine using membrane computing. International Journal of Innovative Computing, Information and Control 5 (4): 899–912.

27.

Huang, L., X. He, N. Wang, and Y. Xie. 2007. P systems based multi-objective optimization algorithm. Progress in Natural Science 17 (4): 458–465.MathSciNetMATHCrossRef

28.

Huang, L., L. Sun, N. Wang, and X. Jin. 2007. Multiobjective optimization of simulated moving bed by a kind of tissue P system. Chinese Journal of Chemical Engineering 15 (5): 683–690.CrossRef

29.

Huang, F., L. Wang, and Q. He. 2007. An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation 186 (1): 340–356.MathSciNetMATHCrossRef

30.

Huang, L., N. Wang, and J. Zhao. 2008. Multiobjective Optimization for Controller Design. Acta Automatica Sinica 34 (4): 472–477.CrossRef

31.

Huang, L., I.H. Suh, and A. Abraham. 2011. Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants. Information Sciences 181 (11): 2370–2391.CrossRef

32.

Huang, X., G. Zhang, H. Rong, and F. Ipate. 2012. Evolutionary design of a simple membrane system. In Membrane Computing (CMC 2011), ed. M. Gheorghe, G. Păun, G. Rozenberg, A. Salomaa, and S. Verlan, 203–214. Lecture Notes in Computer Science Berlin: Springer.CrossRef

33.

Karaboga, D., and B. Basturk. 2007. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In Foundations of Fuzzy Logic and Soft Computing (IFSA 2007), ed. P. Melin, O. Castillo, L.T. Aguilar, J. Kacprzyk, and W. Pedrycz, 789–798. Lecture Notes in Computer Science Berlin: Springer.CrossRef

34.

Krasnogor, N., and J. Smith. 2005. A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation 9 (5): 474–488.CrossRef

35.

Kukkonen, S., and J. Lampinen. 2005. GDE3: the third evolution step of generalized differential evolution. In Proceedings of IEEE Congress on Evolutionary Computation, 443–450.

36.

Leporati, A., and D. Pagani. 2006. A membrane algorithm for the min storage problem. In Membrane Computing (WMC 7), vol. 4361, ed. H.J. Hoogeboom, G. Păun, G. Rozenberg, and A. Salomaa, 443–462. Lecture Notes in Computer Science Berlin: Springer.CrossRef

37.

Li, H., and Q.F. Zhang. 2009. Multiobjective optimization problems with complicated Pareto sets. MOEA/D and NSGA-II, IEEE Transactions on Evolutionary Computation 13 (2): 284–302.CrossRef

38.

Li, B., and Z. Zhuang. 2002. Genetic algorithm based on quantum probability representation. In Intelligent Data Engineering and Automated Learning (IDEAL 2002), vol. 2412, ed. H. Yin, N. Allinson, R. Freeman, J. Keane, and S. Hubbard, 500–505. Lecture Notes in Computer Science Berlin: Springer.CrossRef

39.

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.

40.

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.

41.

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

42.

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. M.J. Gh Păun, A. Pérez-Jiménez, G.Rozenberg Riscos-Núñez, and A. Salomaa, 371–384. Lecture Notes in Computer Science Berlin: Springer.CrossRef

43.

Liu, C., M. Han, and X. Wang. 2011. A multi-objective evolutionary algorithm based on membrane systems. In Proceedings of the 4th International Workshop on Advanced Computational Intelligence, 103–109.

44.

Liu, C., M. Han, and X. Wang. 2012. A novel evolutionary membrane algorithm for global numerical optimization. In Proceedings of the 3rd International Conference on Intelligent Control and Information Processing, 727–732.

45.

Mezura-Montes, E., and C.A.C. Coello. 2005. A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9 (1): 1–17.MATHCrossRef

46.

Nelder, J., and R. Mead. 1965. A simplex method for function minimization. The Computer Journal 7 (4): 308–313.MathSciNetMATHCrossRef

47.

Nishida, T. 2004. An application of P systems: a new algorithm for NP-complete optimization problems. In Proceedings of the 8th World Multi-Conference on Systems, Cybernetics and Informatics, Vol. 5, 109–112.

48.

Nishida, T. 2005. Membrane algorithm: an approximate algorithm for NP-complete optimization problems exploiting P-systems. In Proceedings of 6th International Workshop on Membrane Computing, 26–43.

49.

Nishida, T. 2006. Membrane algorithms. In Membrane Computing (WMC 2005), vol. 3850, ed. R. Freund, Gh. Păun, G. Rozenberg, and A. Salomaa, 55–66. Lecture Notes in Computer Science Berlin: Springer.

50.

Nishida, T. 2006. Membrane algorithms: approximate algorithms for NP-complete optimization problems. In Applications of Membrane Computing, Chapter 11, ed. G. Ciobanu, Gh Păun, and M.J. Pérez-Jiménez, 303–314. Natural Computing Series Berlin: Springer.

51.

Nishida, T. 2007. Membrane algorithm with brownian subalgorithm and genetic subalgorithm. International Journal of Foundations of Computer Science 18 (6): 1353–1360.MathSciNetMATHCrossRef

52.

Nishida, T., T. Shiotani, and Y. Takahashi. 2008. Membrane algorithm solving job-shop scheduling problems. In Proceedings of the 9th International Workshop on Membrane Computing, 363–370.

53.

Păun, G., G. Rozenberg, and A. Salomaa. 2010. The Oxford Handbook of Membrane Computing. New York: Oxford University Press.MATHCrossRef

54.

Peng, H., J. Shao, B. Li, J. Wang, M.J. Pérez-Jiménez, Y. Jiang, and Y. Yang. 2012. Image thresholding with cell-like P systems. In Proceedings of the Tenth Brainstorming Week on Membrane Computing, 75–87.

55.

Peng, H., J. Wang, M.J. Pérez-Jiménez, and P. Shi. 2013. A novel image thresholding method based on membrane computing and fuzzy entropy. Journal of Intelligent and Fuzzy Systems 24 (2): 229–237.

56.

Rao, R., V. Savsani, and D. Vakharia. 2011. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43 (3): 303–315.CrossRef

57.

Robic, T., and B. Filipic. 2005. DEMO: differential evolution for multiobjective optimization. In Proceedings of 3rd International Conference on Evolutionary Multi-Criterion Optimization, 520–533.

58.

Sun, Y., L. Zhang, and X. Gu. 2010. Membrane computing based particle swarm optimization algorithm and its application. In Proceedings of the 5th International Conference on Bio-Inspired Computing: Theories and Applications, 631–636.

59.

Traveling salesman problems. http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/.

60.

Vlachogiannis, J., and K. Lee. 2008. Quantum-inspired evolutionary algorithm for real and reactive power dispatch. IEEE Transactions on Power Systems 23 (4): 1627–1636.CrossRef

61.

Wang, F., Y. Huang, M. Shi, and S. Wu. 2012. Membrane computing optimization method based on catalytic factor. In Advances in Brain Inspired Cognitive Systems (BICS 2012), vol. 7366, ed. H. Zhang, A. Hussain, D. Liu, and Z. Wang, 129–137. Lecture Notes in Artificial Intelligence Berlin: Springer.CrossRef

62.

Wang, H., H. Peng, J. Shao, and T. Wang. 2012. A thresholding method based on P systems for image segmentation. ICIC Express Letters 6 (1): 221–227.

63.

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.

64.

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

65.

Xiao, J., X. Zhang, and J. Xu. 2012. A membrane evolutionary algorithm for DNA sequence design in DNA computing. Chinese Science Bulletin 57 (6): 698–706.CrossRef

66.

Xiao, J., Y. Huang, and Z. Cheng. 2013. A bio-inspired algorithm based on membrane computing for engineering design problem. International Journal of Computer Science Issues 10 (1): 580–588.

67.

Xiao, J., Y. Huang, Z. Cheng, J. He, and Y. Niu. 2014. A hybrid membrane evolutionary algorithm for solving constrained optimization problems. Optik 125 (2): 897–902.CrossRef

68.

Xing, J., and H. Yang. 2012. An optimization algorithm based on evolution rules on cellular system. In Computational Intelligence and Intelligent Systems (ISICA 2012), vol. 316, ed. Z. Li, X. Li, Y. Liu, and Z. Cai, 314–320. Communications in Computer and Information Science Berlin: Springer.CrossRef

69.

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

70.

Yao, X., Y. Liu, and G.M. Lin. 1999. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3 (2): 82–101.CrossRef

71.

Yıldız, A. 2009. An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. Journal of Materials Processing Technology 209: 2773–2780.CrossRef

72.

Yin, X., L. Qiu, and H. Zhang. 2008. A distributed approach inspired by membrane computing for optimizing bijective S-boxes. In Proceedings of the 27th Chinese Control Conference, 60–64.

73.

Zaharie, D., and G. Ciobanu. 2006. Distributed evolutionary algorithms inspired by membranes in solving continuous optimization problems. In Membrane Computing (WMC 7), vol. 4361, ed. H.J. Hoogeboom, Gh. Păun, G. Rozenberg, and A. Salomaa, 536–553. Lecture Notes in Computer Science Berlin: Springer.

74.

Zavala, A., A. Aguirre, and E. Diharce. 2005. Constrained optimization via evolutionary particle swarm optimization algorithm (PESO). In Proceedings of the Genetic and Evolutionary Computation Conference, 209–216.

75.

Zhang, R., and H. Gao. 2007. Improved quantum evolutionary algorithm for combinatorial optimization problem. In International Conference on Machine Learning and Cybernetics, 3501–3505.

76.

Zhang, Y., and L. Huang. 2009. A variant of P systems for optimization. Neurocomputing 72 (4–6): 1355–1360.CrossRef

77.

Zhang, J., and A. Sanderson. 2009. JADE: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13 (5): 945–958.CrossRef

78.

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

79.

Zhang, G., C. Liu, M. Gheorghe, and F. Ipate. 2009. Solving satisfiability problems with membrane algorithm. In Proceedings of the 4th International Conference on Bio-Inspired Computing: Theories and Applications, 29–36.

80.

Zhang, G., L. Hu, and W. Jin. 2010. Resemblance coefficient and a quantum genetic algorithm for feature selection. In Discovery Science (DS 2004), vol. 3245, ed. E. Suzuki, and S. Arikawa, 155–168. Lecture Notes in Artificial Intelligence Berlin: Springer.CrossRef

81.

Zhang, G., Y. Li, and M. Gheorghe. 2010. A multi-objective membrane algorithm for knapsack problems. In Proceedings of the 5th International Conference on Bio-Inspired Computing: Theories and Applications, 604–609.

82.

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

83.

Zhang, G., J. Cheng, and M. Gheorghe. 2011. A membrane-inspired approximate algorithm for traveling salesman problems. Romanian Journal of Information Science and Technology 14 (1): 3–19.

84.

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.MathSciNetMATHCrossRef

85.

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. Chinese Journal of Electronics 13 (18): 1821–1841.MATH

86.

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

87.

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

88.

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

89.

Zhang, G., H. Rong, J. Cheng, and Y. Qin. 2014. A population membrane system-inspired evolutionary algorithm for distribution network reconfiguration. Chinese Journal of Electronics 23 (3): 437–441.

90.

Zhang, G., J. Cheng, M. Gheorghe, F. Ipate, and X. Wang. 2015. QEAM: an approximate algorithm using P systems with active membranes. International Journal of Computers, Communications and Control 10 (2): 263–279.CrossRef

91.

Zhao, J., and N. Wang. 2011. Hybrid optimization method based on membrane computing. Industrial and Engineering Chemistry Research 50 (3): 1691–1704.CrossRef

92.

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

93.

Zhao, J., N. Wang, and P. Zhou. 2012. Multiobjective bio-inspired algorithm based on membrane computing. In Proceedings of International Conference on Computer Science and Information Processing, 473–477.

94.

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.

95.

Zitzler, E., K. Deb, and L. Thiele. 2000. Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation 8 (2): 173–195.CrossRef

Titel: Membrane Algorithms
verfasst von: Gexiang Zhang
Mario J. Pérez-Jiménez
Marian Gheorghe
Verlag: Springer International Publishing
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_3

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