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A New Multi-objective Optimization Algorithm: MOAFSA and its Application

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PL
Wieloobiektowy algorytm optymalizacyjny MOAFSA i jego zastosowanie
Języki publikacji
EN
Abstrakty
EN
This paper proposes a multi-objective artificial fish swarm algorithm (MOAFSA), which imitates the behaviors of fish for local search, uses the quick sort method to get non-dominated solution set, and cuts the external set according to the crowding distance. This paper firstly uses MOAFSA for multi-objective functions test. Results show that MOAFSA has a faster convergence speed and the corresponding Pareto set is more evenly distributed; then MOAFSA is applied in the scheduling optimization of hydropower station reservoir.
PL
Przedstawiono nowy algorytm optymalizacyjny MOAFSA (multiobjective artificial fish swarm algorithm) bazujący na ławicy ryb. Algorytm sprawdzono na przykładzie projektowania obciążeń hydroelektrowni.
Rocznik
Strony
172--176
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China, hxfhuang2005@163.com
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
autor
  • College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
Bibliografia
  • [1] Schaffer, J.D., Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, in Proceedings of the 1st International Conference on Genetic Algorithms. 1985, L. Erlbaum Associates Inc. p. 93-100.
  • [2] Srinivas, N. and K. Deb, Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 1994. 2(3): p. 221-248.
  • [3] Deb, K., et al., A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 2002. 6(2): p. 182-197.
  • [4] Zitzler, E. and L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. Evolutionary Computation, IEEE Transactions on, 1999. 3(4): p. 257-271.
  • [5] E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” in Proc. EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control With Applications to Industrial Problems,K.Giannakoglou,D. Tsahalis, J. Periaux, P. Papailou, and T. Fogarty, Eds., Athens, Greece, Sept. 2001.
  • [6] Xiaolei, L., S. Zhijiang, and Q. Jixin, An optimizing method based on autonomous animats: fish-swarm algorithm. Systems Engineering Theory & Practice, 2002. 22(11): p. 7.
  • [7] Jiang, M., et al., Optimal Multiuser Detection with Artificial Fish Swarm Algorithm, Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, D.-S. Huang, L. Heutte, and M. Loog, Editors. 2007, Springer Berlin Heidelberg. p. 1084-1093.
  • [8] Jiang, M., et al., Image Segmentation with Improved Artificial Fish Swarm Algorithm, Proceedings of the European Computing Conference. 2009. 28: p. 133-138.
  • [9] Shen, W., et al., Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Systems, 2011. 24(3): p. 378-385.
  • [10] Wang, Z., The Optimal allocation of Manufacturing Resources Capabilities in Workshop Based on AFSA, in Advanced Manufacturing Technology, Pts 1, 2, J.T.J.Z.Y.J.S. Han, Editor. 2011. p. 1622-1625.
  • [11] Xiaolei, L., A new intelligent optimization method-artificial fish school algorithm. Ph.D. dissertation. 2003, Zhejiang University.
  • [12] Jiang, M.Y. and D.F. Yuan, Artificial Fish Swarm Algorithm and Its Applications. Proc. of International Conference on Sensing, Computing and Automation. 2006: p. 1782-1787.
  • [13] Zheng, J., et al., A Multi-objective Genetic Algorithm Based on Quick Sort, Advances in Artificial Intelligence, A. Tawfik and S. Goodwin, Editors. 2004, Springer Berlin / Heidelberg. p. 175-186.
  • [14] Deb, K., Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 1999. 7(3): p. 205-230.
  • [15] Zitzler, E., K. Deb, and L. Thiele, Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 2000. 8(2): p. 173-195.
  • [16] Van Veldhuizen, D.A., & Lamont, G. B. , Multiobjective evolutionary algorithm research: A history and analysis. 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-026163e2-b9a8-4967-8dac-472867b14c37
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