A hybrid bat–swarm algorithm for optimizing dam and reservoir operation - Yaseen, Zaher Mundher; Allawi, Mohammed Falah; Karami, Hojat; Ehteram, Mohammad; Farzin, Saeed; Ahmed, Ali Najah; Koting, Suhana; Mohd, Nuruol Syuhadaa; Jaafar, Wan Zurina Wan; Afan, Haitham Abdulmohsin; El-Shafie, Ahmed (Springer Verlag (Germany), 2019)
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. © 2019, Springer-Verlag London Ltd., part of Springer Nature.