fruit fly algorithm

Daily reference evapotranspiration prediction is a decisive and useful tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. The use of hybrid models with the help of climatic factors is an effective method in the daily reference evapotranspiration forecasting process. Therefore, in this study, the ability of the support vector regression model (SVR) and the combined model of support vector regression with the fruit fly algorithm (SVR-FOA) in estimating daily reference evapotranspiration in Ahvaz station during the period of 2000-2022 using four statistical criteria was evaluated. The inputs used included parameters of average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours. The sensitivity analysis of the input parameters using Pearson's correlation coefficient also showed that among the input parameters, the parameters of sunshine hours and relative humidity were effective components in the prediction of evapotranspiration, thus reducing the error in all models. The obtained results showed that the sixth scenario of the SVR-FOA model provided the best performance with the lowest error (1.24 mm/day) compared to all models. Among the scenarios of the SVR model, the third scenario of the SVR model showed the lowest error (1.45 mm/day) compared to other SVR combinations. The results of this research showed that the sixth scenario of the SVR-FOA model had the best performance, and the fruit fly hybrid algorithm improved the performance of the support vector regression in estimating daily reference evapotranspiration.

In this research, after introducing the Whale, Cuckoo search and fruit fly Multi-Objective Optimization Algorithms, their performance individually and compared to each other was evaluated to optimize the Golestan Dam reservoir operation policies with an approach to reduce downstream water demand shortages and flood storage volume management based on reliability, resilience, vulnerability and sustainability criteria. The results showed that the proposed algorithms act differently in optimizing the objective functions. So that the whale multi-objective optimization algorithm leads to better values of objective functions. Regarding to the optimization time, although, there are no significant differences between the cuckoo search and whale algorithms, both perform far better than the fruit fly algorithm and achieve much more convergence over time. When comparing the whale algorithm, as the most efficient algorithm, with the fruit fly algorithm, as the most inefficient algorithm, the model reliability and resilience indices increased by 44% and 52%, respectively, and its vulnerability decreased by 23%, indicating a better performance compared to the other algorithms. Therefore, Whale multi-objective optimization algorithm performs better and converges better than the other algorithms, and cuckoo search and fruit fly multi-objective algorithms ranked second and third.