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An Improved Artificial Fish Swarm Algorithm for Optimal Operation of Cascade Reservoirs | Peng | Journal of Computers
Journal of Computers, Vol 6, No 4 (2011), 740-746, Apr 2011
doi:10.4304/jcp.6.4.740-746

An Improved Artificial Fish Swarm Algorithm for Optimal Operation of Cascade Reservoirs

Yong Peng

Abstract


Based on traditional artificial fish swarm algorithm (AFSA), an improved artificial fish swarm algorithm (IAFSA) is proposed and used to solve the problem of optimal operation of cascade reservoirs. To improve the ability of searching the global and the local extremum, the vision and the step of artificial fish are adjusted dynamicly in IAFSA. Moreover, to increase the convergence speed, the threshold selection strategy is employed to decrease the individual large space gap between before and after update operation in the local update operation. The validity of IAFSA is proved by case study and the threshold parameters of IAFSA are rated.


Keywords


Reservoir operation;Optimization;Hydroelectric power generation

References


[1] Shawwash, Z. K., Siu, T. K. and Russell, S. O. D, “The B.C. Hydro Short Term Hydro Scheduling Optimization Model,” IEEE Transactions on Power Systems, vol. 15, pp. 1125-1131, 2000.
doi:10.1109/59.871743

[2] Xiaohong Guan, Luh, P. B. and Lan Zhang, “Nonlinear approximation method in Lagrangian relaxation-basedalgorithms for hydrothermal scheduling,” IEEE Transactions on Power Systems, vol. 10, pp. 772-778, 1995.
doi:10.1109/59.387916

[3] Franco, P. E. C., Carvalho, M. F. and Soares, S, “A Network Flow Model for Short-term Hydro-dominated Hydrothermal Scheduling Problems,” IEEE Transactions on Power Systems, vol. 9, pp. 1016-1022, 1994.
doi:10.1109/59.317642

[4] Yang Jin-Shyr and Chen Nanming, “Short Term Hydrothermal Coordination Using Multi-pass Dynamic Programming, ” IEEE Transactions on Power Systems, vol. 4, pp.1050-1056, 1989.
doi:10.1109/59.32598

[5] Zhuang F and Galiana F D, “Towards a more rigorous and practical unit commitment by Lagrangian relaxation,” IEEE Transactions on Power Systems, vol. 3, pp. 763-773, 1988.
doi:10.1109/59.192933

[6] Xaiomin Bai and Shahidehpour, S. M, “Hydro-thermal, scheduling by tabu search and decomposition method,” IEEE Transactions on Power Systems, vol. 11, pp. 96-974, 1996.

[7] Ramesh S. V. Teegavarapu and Slobodan P. Simonovic, “Optimal Operation of Reservoir Systems using Simulated Annealing,” Water Resources Management, vol. 16, pp. 401-428, 2002.
doi:10.1023/A:1021993222371

[8] Wardlaw, R., and Sharif, M, “Evaluation of genetic algorithms for optimal reservoir system operation,” J. Water Resour. Plann. Manage., vol. 125, pp. 25–33, 1999.
doi:10.1061/(ASCE)0733-9496(1999)125:1(25)

[9] D. Nagesh Kumar and M. Janga Reddy, “Ant Colony Optimization for Multi-Purpose Reservoir Operation,” Water Resources Management, vol. 20, pp. 879-898, 2006.
doi:10.1007/s11269-005-9012-0

[10] D. Nagesh Kumar and M. Janga Reddy, “Multipurpose Reservoir Operation Using Particle Swarm Optimization,” J. Water Resour. Plann. Manage., vol. 133, pp. 192-201, 2007.
doi:10.1061/(ASCE)0733-9496(2007)133:3(192)

[11] LI Xiao lei, SHAO Zhi-jiang and QIAN Ji-xin, “An optimizing method based on autonomous animals: fish-swarm algorithm,” Systems Engineering Theory & Practice, vol. 22, pp. 32-38, 2002.

[12] PENG Yong, Liang Guo-hua, ZHOU Hui Cheng, “Optimal operation of cascade reservoirs based on improved particle swarm optimization algorithm,” Journal of Hydroelectric Engineering, vol. 28, pp. 49-55, 2009.


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Journal of Computers (JCP, ISSN 1796-203X)

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