A hybrid bat–swarm algorithm for optimizing dam and reservoir operation
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One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world.
KeywordsParticle swarm optimization Multireservoir system Bat algorithm Optimization model
This research was funded by a University of Malaya Research Grant “UMRG” (RP025A-18SUS). The authors are grateful to the University of Malaya, Malaysia, for supporting this study. The authors also would like to acknowledge the Universiti Tenaga Nasional for the financial support under Bold Grant 10289176/B/9/2017/14.
- 1.Chau K (2004) River stage forecasting with particle swarm optimization. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, Berlin, pp 1166–1173Google Scholar
- 3.Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597Google Scholar
- 7.Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: challenges, research directions and future work. Eng Appl Comput Fluid Mech 12(1):411–437Google Scholar
- 17.Bozorg-Haddad O, Azarnivand A, Loáiciga HA (2018) Closure to “development of a comparative multiple criteria framework for ranking pareto optimal solutions of a multiobjective reservoir operation problem” by Omid Bozorg-Haddad, Ali Azarnivand, Seyed-Mohammad Hosseini-Moghari, and Hugo A. Loáiciga. J Irrig Drain Eng 144(4):07018006CrossRefGoogle Scholar
- 26.Hossain MS, El-Shafie A, Mahzabin MS, Zawawi MH (2016) System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm. Neural Comput Appl 30:1–12Google Scholar
- 35.Chakri A, Yang XS, Khelif R, Benouaret M (2017) Reliability-based design optimization using the directional bat algorithm. Neural Comput Appl 30:1–22Google Scholar
- 37.Qaderi K, Akbarifard S, Madadi MR, Bakhtiari B (2017) Optimal operation of multi-Thomas Telford Ltd. reservoirs by water cycle algorithm. In: Proceedings of the Institution of Civil Engineers—Water Management, pp 1–12Google Scholar