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2017 | OriginalPaper | Chapter

A Family of Ant Colony P Systems

Authors : Ping Guo, Mingzhe Zhang, Jing Chen

Published in: Bio-inspired Computing: Theories and Applications

Publisher: Springer Singapore

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Abstract

Ant colony algorithm is a kind of bionic evolutionary algorithm, which is widely used in the field of optimization. Membrane computing is a new computing model, which has the characteristics of distributed, maximal parallelism and non-deterministic. Different with the most current researches that use ant colony algorithm as the sub-algorithm in the framework of the membrane algorithm, this paper considers the realizing ant colony algorithm completely by evolution rules, and we design new ant colony P system \(\varPi _{ACS}\), which includes the membrane structure and evolutionary rules. This paper not only provides a new way to realize the ant colony algorithm, but also lays a foundation for building a general framework for solving optimization problems in membrane computing.

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Dorigo, M.: Optimization, learning and natural algorithms. Thesis Politecnico Di Milano Italy (1992)

Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRef

Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. Biosystems 43(2), 73–81 (1997)CrossRef

Reed, M., Yiannakou, A., Evering, R.: An ant colony algorithm for the multi-compartment vehicle routing problem. Appl. Soft Comput. 15(2), 169–176 (2014)CrossRef

Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Trans. Evol. Comput. 6(4), 333–346 (2010)CrossRef

Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Classification-rule discovery with an ant colony algorithm. In: Khosrow-Pour, M. (ed.) Encyclopedia of Information Science and Technology, 1st edn., pp. 420–424. Idea Group, Hershey (2005). ISBN 1-59140-553-X

Tian, J., Yu, W., Xie, S.: An ant colony optimization algorithm for image edge detection. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence, vol. 2, no. 5, pp. 751–756. IEEE Computer Society (2010)

Fajjari, I., Aitsaadi, N., Pujolle, G., et al.: VNE-AC: virtual network embedding algorithm based on ant colony metaheuristic. In: IEEE International Conference on Communications, vol. 34, no. 17, pp. 1–6 (2011)

Chinese Academy of Sciences, Thomson Reuters: 2015 World’s Most Highly Cited Researchers [EB/OL]. http://thomsonreuters.com/en/articles/2015/research-elite-2015-highly-cited-researchers.html

10.

Rozenberg, G., Bck, T., Kok, J.N. (eds.): Handbook of Natural Computing. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-540-92910-9 MATH

11.

Păun, G., Suzuki, Y., Tanaka, H.: On the power of membrane division in P systems. Theor. Comput. Sci. 324(1), 61–85 (2004)CrossRefMATHMathSciNet

12.

Bernardini, F., Gheorghe, M.: Cell communication in tissue P systems: universality results. Soft Comput. 9(9), 640–649 (2005)CrossRefMATH

13.

Kishan, S.N.: Universality results for P systems based on brane calculi operation. Theor. Comput. Sci. 371(1–2), 83–105 (2007)MathSciNet

14.

Nishida, T.Y.: Membrane algorithm with brownian subalgorithm and genetic subalgorithm. Int. J. Found. Comput. S. 18(6), 1353–1360 (2007)CrossRefMATHMathSciNet

15.

Zhao, J., Wang, N.: Hybrid optimization method based on membrane computing. Ind. Eng. Chem. Res. 50(3), 1691–1704 (2011)CrossRef

16.

Wang, X., Zhang, G., Zhao, J., et al.: A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning. Int. J. Comput. Commun. Control 10(6), 732–745 (2015)CrossRef

17.

Xiao, J., Huang, Y., Cheng, Z., et al.: A hybrid membrane evolutionary algorithm for solving constrained optimization problems. Int. J. Light Electron. Opt. 125(2), 897–902 (2014)CrossRef

18.

Zhang, G., Cheng, J., Gheorghe, M., et al.: A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Appl. Soft Comput. 13(3), 1528–1542 (2013)CrossRef

19.

Du, Q., Xiang, L., Liu, X.: P system based particle swarm optimization algorithm. In: Li, S., Jin, Q., Jiang, X., Park, J.J.J.H. (eds.) ITME 2013. LNEE, vol. 269, pp. 553–563. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7618-0_54 CrossRef

20.

Singh, G., Deep, K.: Hybridization of P systems and particle swarm optimization for function optimization. In: Pant, M., Deep, K., Nagar, A., Bansal, J.C. (eds.) SocProS 2013. AISC, vol. 258, pp. 395–401. Springer, New Delhi (2014). https://doi.org/10.1007/978-81-322-1771-8_34 CrossRef

21.

Yang, J., Zhuang, Y.: An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Appl. Soft Comput. 10(2), 653–660 (2010)CrossRef

22.

Tuba, M., Jovanovic, R., Jovanovic, R.: Improved ACO algorithm with pheromone correction strategy for the traveling salesman problem. Int. J. Comput. Commun. Control 8(3), 477–485 (2013)CrossRef

23.

Guo, P., Liu, Z.J.: An ant system based on moderate search for TSP. Comput. Sci. Inf. Syst. 9(4), 1533–1551 (2012)CrossRef

Title: A Family of Ant Colony P Systems
Authors: Ping Guo
Mingzhe Zhang
Jing Chen
Publisher: Springer Singapore
Book: Bio-inspired Computing: Theories and Applications
Print ISBN: 978-981-10-7178-2

Electronic ISBN: 978-981-10-7179-9

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-981-10-7179-9_14

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