Abstract
Based on the research of the Artificial Fish Swarm Algorithm, this paper put forward an improved hybrid artificial fish algorithm which involves improved preying behavior and improved swarming behavior. Then the performance of the algorithm is improved through introducing genetic crossover operator and a better scope of vision function so as to solve TSP problem. Through the simulation experiment and comparison of improved artificial fish algorithm of literature, the results show that the improved hybrid algorithm in convergence performance and the solution accuracy and convergence rate of the improved artificial fish algorithm is superior to literature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kirkpatr Ick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Holland JH (1962) Outline for a logical theory of adaptive system. J Assoc Comput Mach 3:297–314
Holland JH (1969) A new kind of turnpike theorem. Bull Am Math Soc 75:1311–1317
Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of 1st european conference on artificial life, pp 134–142
Dorigo M (1992) Optimization, learning and natural algorithms. Ph. D. thesis, Department of Electronics, Politecnico di Milano
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. IEEE Service Center, Piscataway NJ, pp 1942–1948
Shi Y, Eberhart RC (1999) Empirical study of Particle swarm optimization. In: Proceedings of congress on evolutionary computation, Washing DC, pp 1945–1950
De Castro LN, Von Zuben FJ (1999) Artificial immune systems: Part I-basic theory and applications. School of Computing and Electrical Engineering Brazil: State University of Campinas .No. DCA-RT 01/99
Li X, Shao Z, Qian J (2002) An optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng Theory Pract 22(11):2–38
Li X, Lu F (2004) Applications of artificial fish school algorithm in combinatorial optimization problems. J Shan Dong Univ (Engineering Science) 34(5):65–67
Xing W, Xie J (1999) Modern optimization method. Tsinghua University Press, Beijing
Fan Y, Wang D, Sun M (2007) Improved artificial fish-school algorithm. J Chongqing Normal Univ (Natural Science Edition) 24(3):23–26
Haibin D (2005) Principle and application of ant colony algorithm. Science Press, Beijing
Zhou Y, Xie Z (2009) Improved artificial fish-school swarm algorithm for solving TSP. Syst Eng Electron 31(6):1458–1461
Zhu M, Ku X (2010) Improved artificial fish school algorithm to solve traveling salesman problem. Appl Res Comput 27(10):3734–3736
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Atlantis Press and the author(s)
About this paper
Cite this paper
Cheng, Cy., Li, HF., Bao, CH. (2016). Hybrid Artificial Fish Algorithm to Solve TSP Problem. In: Qi, E. (eds) Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-145-1_27
Download citation
DOI: https://doi.org/10.2991/978-94-6239-145-1_27
Published:
Publisher Name: Atlantis Press, Paris
Print ISBN: 978-94-6239-144-4
Online ISBN: 978-94-6239-145-1
eBook Packages: Business and ManagementBusiness and Management (R0)