Genetic Algorithm (GA) Method for Optimization of Multi-Reservoir Systems Operation

Document Type : Research Paper


1 Ph.D Student of Water Resources, Dept. of Civil Engineering, Khajeh Nasireddin Toosi University of Technology

2 Faculty Member of Civil Engineering, Khajeh Nasireddin Toosi University of Technology


A Genetic Algorithm (GA) method for optimization of multi-reservoir systems operation is proposed in this paper. In this method, the parameters of operating policies are optimized using system simulation results. Hence, any operating problem with any sort of objective function, constraints and structure of operating policy can be optimized by GA. The method is applied to a 3-reservoir system and is compared with two traditional methods of Stochastic Dynamic Programming and Dynamic Programming and Regression. The results show that GA is superior both in objective function value and in computational speed. The proposed method is further improved using a mutation power updating rule and a varying period simulation method. The later is a novel procedure proposed in this paper that is believed to help in solving computational time problem in large systems. These revisions are evaluated and proved to be very useful in converging to better solutions in much less time. The final GA method is eventually evaluated as a very efficient procedure that is able to solve problems of large multi-reservoir system which is usually impossible by traditional methods. In fact, the real performance of the GA method starts where others fail to function.


1- Labadie, J. W. (2004). “Optimal Operation of Multireservoir Systems: State-of-the-Art Review.” J. Water Resources Planning and Management, ASCE, 130(2), 93-111.
2- Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Mass.
3- Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, John Wiley and Sons. New York.
4- Esat, V., and Hall, M.J. (1994).“Water resources system optimization using genetic algorithms”, Balkema, Rotterdaum the Netherlands., 225-231.
5- Sharif, M., and Wardlaw, R. (2000). “Multireservoir Systems Optimization Using Genetic Algorithms: Case Study.” J. of Computing in Civil Engineering, ASCE,14(4), 255-263.
6- Wardlaw, R., and Sharif, M. (1999). “Evaluation of Genetic Algorithms for Optimal Reservoir System Operation.” J. Water Resources Planning and Management, ASCE, 125(1), 25-33.
7- Oliveira, R., and Loucks, D. P. (1997) “Operating Rules for Multireservoir Systems.” J. Water Resour. Res., 33(4), 839-852.
8- Cai, X. , McKinney, D.C., and Lasdon, L. S. (2001). “Solving Nonlinear Water Management Models Using a Combined Genetic Algorithm and Linear Programming Approach.” J. Advanced in Water Resources, 24, 667-676.
9- Chen, L. (2003). “Real Coded Genetic Algorithm Optimization of Long Term Reservoir Operation.” J. American Water Resources Association (JAWRA), 39(5), 1157-1165.
10- Tung, C., Hsu, S. Liu. C. M., and Li. Jr. Sh. (2003). “Application of the Genetic Algorithm for Optimizing Operation Rules of the LiYuTan Reservoir in Taiwan.” J. of American Water Resources Association (JAWRA), 39(3), 649-657.
11- Momtahen, S., Dariane, A. B., and McKey, M. (2005). “Direct Search Approach Using Genetic Algorithm for Optimization of Water Reservoir Operating Policies.” J. Water Resources Planning and Management. in press.
12- Wurbs, R. A. (1993). “Reservoir-system Simulation and Optimization Models.” J. Water Resources Planning and Management , ASCE, 119(4), 455-472.
13- ReVelle, C., Joeres, E., and Kirby, W. (1969). “The Linear Decision Rule in Reservoir Management and Design, 1, Development of the Stochastic Model.” J. Water Reours. Res., 5(4), 767-777.
14- Loucks, D.P. (1970). “Some Comments on Linear Decision Rules and Chance Constraints.” J. Water Resour. Res., 6(2), 668-671.
15- Bhaskar, N. R., and Whitlatch, E. E. (1980). “Derivation of Monthly Reservoir Release Policies.” J. Water Resour. Res., 16(6),987-993.
16- Young, G. K. (1967). “Finding Reservoir Operating Rules.” J. of Hydraulic Division, ASCE, HY6, 297-321.
17- Butcher, W. S. (1971). “Stochastic Dynamic Programming for Optimum Reservoir Operation.” Water Resource Bulletin, 7(1), 115-123.
18- Torabi, M., and Mobasheri, F. (1973). “A Stochastic Dynamic Programming Model for the Optimum Operation of a Multi-Purpose Reservoir.” Water Resource Bulletin, 9(6), 1089-1099.
19- Eshelman, L. J., and Shaffer, J. D. (1993). Real-coded genetic algorithms and interval schemata, In : Foundation of genetic algorithms 2 (FOGA-2), Morgan Kaman Publishers, San Mateo, CA, 187-202.