Evaluation of GA and PSO optimization algorithms in operation of multi-reservoir systems Case study: Gorgan-Rood basin dams

Document Type : Complete scientific research article


1 Faculty member

2 water eng. department, IKIU

3 water eng. group. Exeter, England


Background and purpose: Optimal utilization of water resources systems and the formulation of appropriate rules and policies for the exploitation of reservoirs have been considered by water resource experts in recent years and extensive research has been carried out on them. Although much progress has been made in terms of problem-solving strategies and computational tools, the problem of optimizing the operation of a multi-reservoir systems due to the effect of upstream storage capacities on low-drain inputs is so complicated. Routine optimization methods due to high constraints, discontinuous space and non-linear nature of water resource management issues are not a good tool for solving such problems. For this reason, the metaheuristic optimization algorithms have been considered by researchers. The purpose of this study was to examine and compare the results of applying GA and PSO methods in optimum utilization of Golestan and Bustan multi-grout systems in Gorgan Rood watershed using the reliability index in climate change conditions.

Methods: In this research, the performance of the GA and PSO in solving the problem of optimizing the operation of a multi-reservoir system including Bostan and Golestan dams located in Gorgan-Rood watershed has been studied and compared. The survey of the entrance to the two dam reservoirs in the year 2014-2015 shows that due to the climate change, the annual input to the Bostan and Golestan dams has decreased by 17% and 60%, respectively.
Genetic algorithm is a parallel and guided search based on the theory of evolution. The operators of the GA algorithm include selection, crossover, and mutations that are used up to the next generation, respectively. In PSO optimization algorithm, based on the birds and fishes movements, a number of particles are propagated in the search space and the value of the objective function is calculated in proportion to the position of each particle. Then the new particle position is calculated using the combination of current particle locations and the best place previously used.
Achievements: The best answer of the PSO algorithm during the 10 runs is 909.95 and the worst is the equal to 930.53, while the best answer of the GA algorithm during the 10 run is 931.17 and the worst It was 957.32. The comparison of the mean of the answers also show that the PSO algorithm has a 3% advantage over GA.
Conclusion: The PSO algorithm has a better performance than GA, so that the PSO algorithm with a reliability of 49.38% has a better performance than the GA algorithm with a reliability of 48.44%.


1.Ahmadianfar, A., and Adib, A. 2016.
Optimizing hydropower dams operation
using hybrid of PSO and GA (Case study:
Dez Dam). Irrigation Science &
Engineering, 38: 3. 63-71. (In Persian)
2.Eberchart, R., and Kennedy, J. 1995.
Particle swarm optimization. IEEE
InternationalConference on Neural
Networks Perth, Australia. Pp: 1942-1947.
3.Esat, V., and Hall, M.J. 1994. Water
resources system optimization using
genetic algorithms. Proc. 1st Int. Conf. on
Hydroinformatics, Balkema, Rotterdam,
Netherlands. Pp: 225-231.
4.Goldberg, D. 1989. Genetic algorithms
in search optimization and machine
learning. Addison-Wesley Longman
Publishing Company. Boston. 403p.
5.Hosseini-Moghari, S.M., and Banihabib
M.E. 2014. Optimizing operation of
reservoir for agricultural water supply
using firefly algorithm. J. Water Soil
Resour. Cons. 3: 4. 17-31. (In Persian)
6.Iran Water Resources Management Co.
2012. Annual report on the status of dams
operating in different months. (In Persian)
7.Kalbali, E., Sabuhi Sabuni M., and
Ahmadpur Borazjani, M. 2016. Strategies
of Voshmgir dam water allocation using
two-stage stochastic programming. J.
Water Soil, 30: 6. 1832-1847. (In Persian)
8.Karimi, M., Ahrar Yazdi, B.N., and Ahrar
Yazdi, B.D. 2017. Examining the
performance of two pso and g-algorithms in
optimizing the CGAM issue. Mechanical
Engineering Sharif, 33: 1. 129-136.
9.Kjeldsen, T.R., and Rosbjerg, D. 2004.
Choice of reliability, resilience and
vulnerability estimators for risk
assessments of water resources systems.
Hydrol. Sci. J. 49: 757-767.
10.Moeini, R., and Afshar, M.H. 2009.
Application of an ant colony
optimization algorithm for optimal
operation of reservoirs: A comparative
study of three proposed formulations.
J. Sci. Iran. 16: 4. 273-285.
11.Moeini, R. 2016. Performance evaluation
of the ant Colony optimization algorithm
for the optimal operation of a
multi-reservoir system: comparing four
algorithms. Iran-Water Resources Research,
11: 2. 29-46. (In Persian)
12.Moghadam, A., Alizadeh A., Farid, A.,
Ziaei, A.N., and Fallah, D. 2013. The
application of improved particle swarm
optimization algorithm in design of
water distribution systems. Iran. J. Irrig.
Drain. 7: 3. 389-401. (In Persian)
13.Moghaddam, A., Montaseri, M., and
Rezaei, H. 2016. The Application of
GA, SMPSO and HGAPSO in optimal
reservoirs operation. J. Water Soil.
30: 4. 1102-1113.
14.Momtahen, S., and Borhani Darian, A.R.
2006. Genetic algorithm (GA) method
for optimization of multi-reservoir
systems operation. J. Water Wastewater.
29: 2. 11-20.
15.Norozi, B., Barani, Gh.A., Meftah
Halghi, M., and Dehghani, A. 2011.
A multi-reservoir system operation
optimization using multi population
genetic algorithms (Case study:
Golestan and Voshmgir reservoirs). J.
Water Soil Cons. 18: 2. 43-61. (In Persian)
16.Zadesh Pargo, R., Mazandarani Zadeh
H., and Daneshkar Araste, P. 2015.
Subsurface Drainage System Design
to Minimize Construction Costs with
Steady-State Consideration. Water
Research in Agriculture, 29: 1. 117-128.
(In Persian)