Optimization of Reservoir Operation using New Hybrid Algorithm

Due to the scarcity of fresh water resources, exploiting dams' reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers' water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algorithm (AFSA) and Particle Swarm Optimization Algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid Algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant's objective function.