gravitational search algorithm

Considering the recent human activities and the resulting climate change in optimizing the operation of the dam reservoir, the effects of climate change should be noticed. In this research, in order to extract command curves by dolphin echolocation and gravitational search algorithms, the monthly inflow of the reservoir, the reservoir storage volume, and the downstream demand of the reservoir in case of climate change were calculated .The optimal output values of the reservoir of Lar Dam (located in Larijan, Amol City) were determined by the approach of minimizing the total square of the monthly relative deficiencies in supply demand and climate change conditions based on the river flow According to the research, by using HADCM3 and scenarios RCP2.6, RCP4.5, and RCP 8.5, climate change has increased the maximum temperature by 5%, 5.2%, and 6.2%, respectively. It has increased the minimum temperature by 3.5%, 5.6%, 5.17%, and increased precipitation by 8.5%, 9.5%, and 13%, respectively. In addition, the runoff from the intermediate scenarios indicates an increase of 3.3% compared to the base period. Moreover, to examine the water allocation policies required downstream, two future and basic conditions are considered. In this study, reservoir efficiency indices in the conditions of (future) climate change and their corresponding values ​​in the base period were compared. The execution results of each of the algorithms show that the execution speed of the DE algorithm is much higher than the GSA algorithm, as well as, in the conditions of climate change, the reliability index in the dolphin echolocation and gravity search algorithms has increased.  9.73 and 12.46% Vulnerability has decreased by 21.4% and 26.51%, respectively, and reversibility has increased by 18.27% and 17.64%, respectively. The execution results of each of the algorithms show that the execution speed of the DE algorithm is much higher than the GSA algorithm. Furthermore, in the conditions of climate change, the reliability index in the dolphin echolocation and gravity search algorithms has increased 9.73 and 12.46% Vulnerability has decreased by 21.4% and 26.51%, respectively, and reversibility has increased by 18.27% and 17.64%, respectively.

Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving structure-related problems is presented. The main purpose of this paper is to present an algorithm that addresses the major drawbacks of commonly-used algorithms including the Grey Wolf Optimization Algorithm (GWO), the Gravitational Search Algorithm (GSA), and the Particle Swarm Optimization Algorithm (PSO), and at the same time benefits from a high convergence rate. Also, another advantage of the proposed CGPGC algorithm is its considerable flexibility to solve a variety of optimization problems. To this end, we were inspired by the GSA law of gravity, the GWO's top three search factors, the PSO algorithm in calculating speed, and the cellular machine theory in the realm of population segmentation. The use of cellular neighborhood reduces the likelihood of getting caught in the local optimal trap and increases the rate of convergence to the global optimal point. Achieving reasonable results in mathematical functions (CEC 2005) and spatial structures (with a large number of variables) in comparison with those from GWO, GSA, PSO, and some other common heuristic algorithms shows an enhancement in the performance of the introduced method compared to the other ones.

Reliable and accurate streamflow forecasting plays a crucial role in water resources systems (WRS) especially in dams operation and watershed management. However, due to the high uncertainty associated WRS components and nonlinear nature of streamflow generations, the realistic streamflow forecasts is still one of the most challenging issue in WRS. This paper aimed to forecast one-month ahead streamflow of Karun river (Iran) by coupling an artificial neural network (ANN) with an improved binary version of gravitational search algorithm (IBGSA), named ANN- IBGSA. To this end, the best lag number for each predictor at Poleshaloo station was firstly selected by auto-correlation function (ACF). The ANN-IBGSA was used to minimize the sum of RMSE and R2 and to identify the optimal predictors. Finally, to characterize the hydro-climatic uncertainties associated with the selected predictors, an implicit approach of Monte-Carlo simulation (MCS) was applied. The ACF plots indicated a significant correlation up to a lag of two months for the input predictors. The ANN-IBGSA identified the Tmean (t-1), Q(t-1) and Q(t) as the best predictors. Findings demonstrated that the ANN-IBGSA forecasts were considerably better than those previously carried out by researchers in 2013. The average improvement values were 9.91%, 11.85% and 9.13% for RMSE, R2 and MAE, respectively. The Monte-Carlo simulations demonstrated that all of forecasted values lie within the 95% confidence intervals.