meta-heuristic algorithms

This research aimed to select essential features for modeling the cost of pressurized irrigation systems using the data of 515 drip irrigation projects in four parts, including the cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and operation on-farm and pumping station (TCI), and total cost (TCT). In the first stage, a database including 39 features influencing the cost of the mentioned sectors was prepared and the price of all projects (2006 to 2019) was updated for the base year of 2021. Then, feature selection was done with different algorithms in MATLAB environment and in two parts including (1) all features (39 features before and after the design stage) and (2) 18 features before the design phase (BD). The results showed that the amounts of RMSE and R2 for all the features were equal to 0.007 and 0.92, respectively, and for the BD section, they were equal to 0.003 and 0.89, respectively. Among the different algorithms for feature selection, support vector machine (SVM) and optimization algorithms (Wrapper) were identified as the best learner and feature selection method, respectively. The results of the evaluation criteria showed that the two LCA and FOA algorithms achieved the best estimation, and their error criterion in all the features were 0.0020 and 0.0018, respectively, while their correlations were 0.94 and 0.94. In the BD features, these criteria were 0.0006 and 0.95 for both algorithms, respectively. Finally, in the all features section, 10 out of 39 features and for BD section, 8 out of 18 were selected as the most effective features. The results of choosing the most effective features that affect the cost of different parts of the drip irrigation system can make the cost modeling of the systems simpler and faster and, while being useful for research works, it facilitates estimation and management of costs before implementation of each project.

Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

In many areas of Iran, there is no detailed information on the amount of erosion, sediment transport and sedimentation of rivers, and in many cases, there are many difference between measurements. Due to the fact that the flow regime and consequently the sediment regime in the watersheds are not constant, the prediction of sediment rate helps to estimate the sediment accumulated behind the structures, specially the dams, and determine the dead volume of reservoirs in the future months, and by adopting timely arrangements facilitate the deposition management to a certain extent. In this research, three optimization algorithms including Genetic Algorithm (GA), Gray Wolf Optimizer (GWO) and Election Algorithm (EA) were used to predict the suspended sediment load of the rivers. In order to evaluate the performance of the algorithms, three statistics consists of R2, RMSE and NSE were used. The suspended sediment load of sedimentary station located in the Zarrineh-Rood river during the 2005-2015 are used as a case study. The results show GWO algorithm with values R2=0.96, RMSE=0.022 and NSE=0.74 has a very high accuracy compared to other algorithms used.

Use of the curve gradient of the Soil Water Retention Curves (SWRC) in the inflection point (S Index) is one of the main indices for assessing the soil quality for management objectives in agricultural and garden lands. In this study Anneling Simulated – artificial neural network (SA-ANN) hybrid algorithm was used to identify the most effective soil features on estimation of S Index in Jiroft plain. For this purpose, 350 disturbed and undisturbed soils samples were collected from the agricultural and garden lands and then some physical and chemical soil properties including Sand, Silt, Clay percent, Electrical Conductivity at saturation, Bulk Density, total porosity, Organic Mater, and percent of equal Calcium Carbonate were measured. Moreover, the soil moisture amount was determined within the suctions of 0, 10, 30, 50, 100, 300, 500, 1000, 1500 KP using pressure plate. Then, the determinant features influencing the modeling of S Index were derived using SA-ANN hybrid algorithm. The results indicated that modeling precision increased by reducing the input variables. According to the sensitivity analysis, the Bulk Density had the highest sensitivity coefficient (sensitivity coefficient=0.5) and was identified as the determinant feature for modeling the S Index. So, since increasing the number of features does not necessarily increase the accuracy of modeling, reducing input features is due to cost reduction and time-consuming research.

In recent decades, Meta-heuristic algorithms has played an effective role in solving different engineering problems such as optimal operation of reservoirs. Owing to the complexity of water resources management problems, as well as, the daily growing need for the development and expansion of these methods, in this research, a model based on Symbiotic Organisms Search (SOS) algorithm was developed for modeling the optimal operation of complex multi-reservoirs systems. In the first step, the performance of the method was successfully assessed through several benchmark functions. Then it was used for the monthly operation of Tangemashure, Sazbon and Karkheh reservoirs located in Karkheh basin. The optimal allocation were considered for meeting the irrigation demands of 4 agricultural regions, and priority was with allocation of water for the environmental demands., for a 5 year period (from 1980-81 to 1984-85). The results of SOS algorithm were compared with other developed evolutionary algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results indicated that, optimized operation policy through SOS algorithm with the sustainability index of 99.99, 99.11, 82.92 and 79.47 percent for Sazbon, Tangemashure, upstream and downstream of Karkheh reservoirs was more appropriate performance as compared to GA and PSO algorithms in optimal operation of multi-reservoirs systems.