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Accurate streamflow prediction information is of great importance for water resource planning and management. The goal of this research is to develop a hybrid model for forecasting short-term runoff time series, where the variational mode decomposition (VMD) is first used to decompose the original nonlinear natural streamflow into numerous subcomponents with different frequencies and resolutions. Second, the extreme learning machine (ELM) is used to excavate the complicated input–output relationship hidden in each subcomponent, and the emerging sine cosine algorithm (SCA) is used to determine the suitable network parameter for each ELM model. Finally, the forecasting results of all the modelled subcomponents are summarized to form the forecasting result for original streamflow. Based on several statistical evaluation measures, the feasibility of the hybrid method is investigated in runoff forecasting for Danjiangkou Reservoir in China. The results indicate that the hybrid method can produce superior forecasting results compared to several control methods, providing accurate streamflow prediction information for operators. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is an emerging meta-heuristic method for the complicated global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. To improve the SCA performance, this paper develops a modified sine cosine algorithm coupled with three improvement strategies, where the quasi-opposition learning strategy is used to balance global exploration and local exploitation; the random weighting agent produced by multiple leader solutions is integrated into the agent's evolution equation to improve the convergence rate; the adaptive mutation strategy is designed to increase the swarm diversity. The proposed method is compared with several famous evolutionary methods on 12 classical test functions, 24 CEC2005 composite functions and 30 CEC2017 benchmark functions. The results show that the proposed method outperforms several control methods in both solution quality and convergence rate. Then, the long-term operation optimization of multiple hydropower reservoirs in China is chosen to testify the engineering practicality of the developed method. The simulation results indicate that in different scenarios, the proposed method can produce satisfying scheduling schemes with better objective values compared with several existing evolutionary methods. Hence, a novel optimizer is provided to handle the complicated engineering optimization problem. [ABSTRACT FROM AUTHOR]

• Adaptive sine cosine algorithm is proposed for hydropower operation. • Elite mutation strategy is introduced to increase individual diversity. • Simplex dynamic search strategy is used to improve solution accuracy. • Neighborhood search strategy is used to improve the convergence rate. • Simulations demonstrate the superiority of the presented method. Recently, the multiple hydropower reservoirs operation optimization is attracting rising concerns from researchers and engineers since it can not only improve the utilization efficiency of water resource but also increase the generation benefit of hydropower enterprises. Mathematically, the reservoir operation problem is a typical multistage constrained optimization problem coupled with numerous decision variables and physical constraints. Sine cosine algorithm (SCA), a new swarm-based method, has the merits of clear principle and easy implementation but suffers from the premature convergence and falling into local optima. To improve the SCA performance, this paper proposes an adaptive sine cosine algorithm (ASCA) where the elite mutation strategy is used to increase the population diversity, the simplex dynamic search strategy is used to enhance the solution quality, while the neighborhood search strategy is used to improve the convergence rate. The simulations of 25 test functions show that ASCA outperforms several existing methods in both convergence rate and solution quality. The results of a real-world hydropower system in China demonstrate that ASCA betters the SCA method with obvious increase in power generation. Thus, the main contribution of this study is to provide an effective optimizer for multiple hydropower reservoirs operation. [ABSTRACT FROM AUTHOR]

Influenza epidemic is a serious threat to the entire world, which causes thousands of death every year and can be considered as a public health emergency that needs to be more addressed and investigated. Forecasting influenza incidences or confirmed cases is very important to do the necessary policies and plans for governments and health organizations. In this paper, we present an enhanced adaptive neuro-fuzzy inference system (ANFIS) to forecast the weekly confirmed influenza cases in China and the USA using official datasets. To overcome the limitations of the original ANFIS, we use two metaheuristics, called flower pollination algorithm (FPA) and sine cosine algorithm (SCA), to enhance the prediction of the ANFIS. The proposed FPASCA-ANFIS is evaluated using two datasets collected from the CDC and WHO websites. Furthermore, it was compared to some previous state-of-the-art approaches. Experimental results confirmed that the FPASCA-ANFIS outperformed the compared methods using variant measures, including RMSRE, MAPE, MAE, and R 2 .