Abstract
After the shortcoming of the optimization algorithm on using fishing strategy (FSOA) being analyzed in this paper, an improving FSOA optimization is presented. The main approach of this optimization is that, about the aspect of the choice of detecting points, every fisherman selects his first detecting-point in his positive direction randomly, and then the others are determined through the orthogonal transform of the first detecting-point. And about the aspect of the searching strategy, that only the fisherman who is at the present optimal point that takes the strategy of constricted search, and the others make use of the strategy of moving search. It shows, from the experimental imitating study of some typical benchmark functions’ optimization, that the improving FSOA is effective and feasible for solving the optimal solution of the complex functions.
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References
Holland, J.H.: Adaptation in Nature and Artificial Systems. MIT Press, Cambridge (1992)
Goldberg, D.E.: Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufman Publishers, San Francisco (2001)
Dorigo, M., Maniezzo, V., Coloria, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics: PartB 26(1), 29–41 (1996)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling saleman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Chun, J.S.: Shape optimization of electro-magnetic devices Using immune algorithm. IEEE Transactions on Magnetics 33(2), 1876–1879 (1997)
de Castro, L.N.: Learning and optimization using the clonal Selection principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems (2001)
Li, X.-l., Shao, Z.-j., Qian, J.-x.: An optimizing method based on autonomous animats:Fish-swarm algorithm. Systems Engineering-theory & Practice 22(11), 32–38 (2002) (in Chinese)
Chen, J.-r., Wang, Y.: An optimization approach on using fishing strategy. Computer Engineering and Applications 45(9), 53–56 (2009) (in Chinese)
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© 2011 Springer-Verlag Berlin Heidelberg
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Wang, Y., He, D., Guan, Y., Luo, J. (2011). An Improving FSOA Optimization by Using Orthogonal Transform. In: Shen, G., Huang, X. (eds) Advanced Research on Electronic Commerce, Web Application, and Communication. ECWAC 2011. Communications in Computer and Information Science, vol 144. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20370-1_11
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DOI: https://doi.org/10.1007/978-3-642-20370-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20369-5
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