Application of Cat Swarm Optimization Algorithm for Optimal Reservoir Operation

The scarcity of water resources throughout the world has caused many complexities in meeting water demands, which in turn has created a tendency toward developing more efficient and effective methods for optimum operation of reservoirs. In this study, the cat swarm optimization (CSO) algorithm is applied to determine optimal operation of reservoir systems (a single-reservoir system and a hypothetical four-reservoir system). Comparison with the commonly used genetic algorithm (GA) demonstrates the superiority of this metaheuristic algorithm. For the single-reservoir system, the global optimum of 1.213 was computed using the nonlinear programming method, whereas the average objective-function values for 10 runs of the CSO algorithm and GA were 1.222 and 1.635, respectively. The CSO algorithm scored a convergence rate of 99.58% compared with 78.76% by GA, and a coefficient of variation that was 1/31 that of the GA in 10 runs. In the four-reservoir system, the convergence rate of the CSO algorithm was 99.97% compared with 98.47% by GA, with average objective-function values of 307.76 and 303.59, respectively. The results for the mathematical test functions and operations of the reservoir systems demonstrated the superior performance and high efficiency of the CSO algorithm in finding the global optimization solutions.