Development of the FA-KNN hybrid algorithm and its application to reservoir operation

This study presents a method to address the issue of burdensome computations in water resources optimization based on a hybrid algorithm derived from the firefly algorithm (FA) and the K-nearest neighbor (KNN) algorithm, herein named the FA-KNN algorithm. The FA-KNN algorithm introduced in this work is tested with three standard test problems (the Ackley, Rosenbrock, and Sphere problems), and with a reservoir operation problem that minimizes the relative agricultural water-supply deficit under baseline and climate-change conditions. The efficiency indexes of the reservoir system are calculated to evaluate the performance of the FA-KNN algorithm and its accuracy. The results demonstrate the operational policy obtained with the FA-KNN algorithm has better performance in terms of computational burden than the FA's. This work's findings establish the FA-KNN hybrid algorithm reduces the computational time by 60% with acceptable accuracy compared with the FA algorithm. The findings indicate a reduction in run-time of 99.5, 94, and 92% for solving the Ackley, Rosenbrock, and Sphere test problems achieved with the FA-KNN algorithm while maintaining a high level of accuracy when contrasted with solutions derived from both deterministic methodologies and the FA approach. The volumetric reliability and flexibility in the reservoir problem calculated under the baseline conditions outperformed those obtained with the climate change conditions by 10 and 3.5%, respectively. Moreover, a notable discrepancy emerged in terms of the main simulator's invocation frequency between the FA-KNN and FA methods (the former exhibited a mere 0.3 ratio compared to the latter). The application of the FA-KNN approach yielded a reduction exceeding 60% in run-time for the reservoir problem.