meta-heuristic algorithm

Evaporation from lakes and reservoirs of dams as well as soil is one of the most important processes in hydrological engineering. One of the procedures required for proper and effective management of reservoirs, water resources, basin stability, and agricultural operations is the accurate prediction of evaporation. Weather phenomena that are non-stationary, unpredictable, and non-linear generally have an impact on evaporation. It is evident that these issues hinder the development of precise prediction models. Evaporation is a natural process based on energy supply and air exchange, during which molecules and atoms find the necessary energy to leave the liquid phase and enter the gas phase. On the other hand, climate changes can affect the evaporation parameter, so the prediction of evaporation from the reservoirs of dams is vital in the discussion of water resources management

This investigation uses the SVR-ABC, Wavelet-SVR, and SVM models with six wavelet functions to predict the amount of evaporation from the Dez dam reservoir in Khuzestan province, Iran. The data series from 1350–1396 for 46 years arises from the Dez dam meteorological station in Iran. The study also uses precipitation, maximum temperature, minimum temperature, average temperature, absolute maximum temperature, and absolute minimum temperature and Using the findings of Shannon's entropy, 5 parameters Tmax, Tmin, Tave, Tamax, Tamin are divided into 5 groups with 1 parameter, 2 parameters, 3 parameters, 4 parameters and 5 parameters respectively as the most effective parameters for scenario planning, which in total 28 scenarios are studied.

According to modeling results based on RMSE, MAE and WI evaluation indices show that the performance of SVR-ABC meta-heuristic model with RMSE=82.219, MAE=53.977 and WI=0.815 is better than Wavelet-SVR model with RMSE=93.637, MAE= 69.360 and WI=0.762. Additionally, based on the violin diagram, the single SVR model with periodic and non-periodic inputs estimated the monthly evaporation lower than the observed values, and as a result, the average data has decreased compared to the observed values.

Furthermore, according to the results of the six wavelet functions that were used in the study, the Wavelet-SVR axis decomposition model with the Haar wavelet function at decomposition level 1 and values of 93.637 mm/month, 69.360 mm/month, and 0.762, respectively, shows the most appropriate result among the six wavelet functions based on the RMSE, MAE, and WI evaluation indices.

Accurate determination of land boundaries is a vital step in identifying land use and planning for its management. For cropland, this mapping allows farmers and agribusinesses to better estimate land area for effective use of agricultural inputs, such as seeds, pesticides, fertilizers and other resources, and to optimize production and post-production activities. With these interpretations, identifying the precise edges of a crop field determines the success of a program developed using computer vision and machine vision tools. According to the experiments, the Canny detector performs better than other traditional detectors, therefore, in this article, it has been tried to present new methods to improve the performance of the Canny detector by using two meta-heuristic algorithms of ant colony and thermal simulator. To evaluate the proposed methods, we have used three evaluation criteria: MSE, PSNR, and SSIM. The results show that the meta-heuristic algorithms used have made the Canny detector more optimal. When we used the ant colony algorithm, the edges were shown well, and when we used the simulated annealing algorithm, in addition to the edges, the image texture was also preserved.

Agricultural production involves a series of tasks including tillage, planting, and harvesting, which must be done at the right time for each region and type of product. Failing to complete these tasks on time can lead to a decrease in yield. Farmers may wrongly attribute this to factors such as infertile land, pests, diseases, and uneven rainfall distribution. However, this decrease in yield may not always be evident or tangible. To avoid such losses and unforeseen expenses, it is crucial to plan agricultural mechanization projects using the principles of project control. Agricultural projects, like industrial projects, must be carried out in the correct order and at the right time to achieve optimal results. Given the limited availability of resources for mechanization projects, it is imperative to meticulously plan activities to ensure that they are carried out on time and with maximum utilization of resources. To address these challenges, researchers have used meta-heuristic methods in project control, such as the colonial competition algorithm, which has been proven effective in solving the issue of scheduling projects with limited resources. The algorithm has been tested across various industrial activities and projects, and its performance in scheduling the Resource-Constrained Project Scheduling Problem (RCPSP) has been validated by researchers globally.

There is a scheduling issue regarding limited resources in agriculture, and this study presents a novel approach using the imperialist competitive algorithm (ICA). The algorithm not only explores a wider solution space but also strives to minimize deviation from the optimal solution, thereby improving the success rate of the proposed method. This research focuses on two dominant products, wheat and rapeseed, produced in Moghan Agriculture and Industry located in Northwest Iran. To evaluate the effectiveness of ICA, we compared it with other well-known meta-heuristic algorithms. We successfully resolved the problem of project scheduling problem with limited resources by implementing the imperialist competitive algorithm. Our findings have shown that this approach not only significantly increased efficiency but also outperformed other algorithms.

In this study, we assessed the efficiency of meta-heuristic methods in solving the RCPSP, which can be useful in optimizing the timeliness of project execution, especially for large-scale projects. Some meta-heuristic methods are only useful for smaller problems, while others can provide near-optimal solutions for larger problems, making them suitable for RCPSP. The algorithm explores a wide range of solutions and avoids premature convergence and getting stuck in local optima, unlike other algorithms such as the genetic algorithm. Optimization reduced the required budget and shortened the duration by 42 days for wheat and 25 days for rapeseed.

We utilized the colonial competition algorithm to address the RCPSP problem in agricultural mechanization projects for two agricultural products in Moghan. Our results show that the proposed algorithm converged and reached the optimal solution. The proposed algorithm was compared with other algorithms and it outperformed them.

The soil water curve is one of the most critical soil hydraulic characteristics. This characteristic is used to determine soil water in the field capacity point and the permanent wilting point (PWP) beside it has a vital role in the application of soil water models in the study of soil-plant-water relationships. This curve is known as the quality soil index which has an effective role in the explanation of agricultural, ecological, and environmental problems. Impressive and efficient management of soil and water resources, water flow and solute transport survey, soil pollution, and contaminant leakage into water sources are dependent upon the accurate estimation of soil water curve parameters. Moreover, this index has a functional role in applying numerical and hydrological models. On the other hand, to better identify and understand its role, different models were provided to describe this curve mathematically. The efficiency of these models depended on the accuracy of estimated parameters in the model structure that was defined. Soil water curve is known as a non-linear relationship that is used to describe the relation between soil and water content or degree of soil saturation. The soil water curve provides essential information for using irrigation methods and about soil resistance and soil mechanical properties. In this research, the performance trend of two meta-heuristic algorithms, including the differential evolution (DE) and particle swarm optimization (PSO), was studied to estimate hydraulic parameters of soil water curves based on the van Genuchten and the Brooks and Cory models in four soil texture classes; loam, silt loam, sandy loam, and sandy clay loam. Besides, this study evaluated the performance of the meta-heuristic algorithm to RETC software. This software has a non-linear square local algorithm. This study can evaluate the ability of the meta-heuristic algorithms to estimate parameters for exponential relationships and nonlinear models.

At the agricultural farm of the University of Birjand, a study was conducted to analyze soil water content in different texture classes. The research involved the random selection of four soil texture classes and the random sampling of 20 points from each class. The soil water content was measured using a sandbox and pressure plate device, covering a broad suction range of 0-15000 cm. In the first phase, soil water curve parameters were estimated for each soil texture using the van Genuchten model and the Brooks and Cory model in the RETC software. Subsequently, the Matlab desktop environment was utilized to apply meta-heuristic algorithms (DE and PSO) to estimate the soil water curve parameters based on the two models. An objective function was defined to minimize the Root Mean Square Error (RMSE) of the meta-heuristic algorithms' performance. Finally, the study compared the performance of the meta-heuristic algorithms (DE and PSO) with the RETC software in estimating soil water curve parameters based on the van Genuchten and Brooks and Cory models, using statistical indices such as RMSE and R2. The soil texture classes play a crucial role in influencing soil water content and nutrient retention, making them an essential factor in agricultural management and crop suitability. The study's findings can contribute to a better understanding of soil water dynamics and the development of improved agricultural practices.

The obtained results of the statistical indices (RMSE and R2) showed that the least value of RMSE was acquired by the differential evolution algorithm (DE) performance. The values of RMSE during the application of the DE algorithm as an estimated method based on the van Genuchten model were 0.0008, 0.0005,0.0004, and 0.0006 also based on the Brooks and Cory were 0.006, 0.006, 0.005, and 0.0005 in sandy clay loam, sandy loam, loam, and silt loam respectively. Also, the highest value of the R2 index was obtained equal to 0.995, 0.996, 0.994, and 0.994 by the utilization of the DE algorithm based on the van Genuchten model in the sandy clay loam, sandy loam, loam, and silt loam respectively. The values of RMSE by the utilization of the PSO algorithm based on the van Genuchten model were 0.0021, 0.006, 0.0057, and 0.006 in the sandy clay loam, sandy loam, loam, and silt loam classes respectively. The highest and lowest values of the RMSE and R2 indices by the application of RETC software were obtained equal to 0.017 and 0.912 (sandy clay loam), 0.01and 0.963 (sandy loam), 0.085 and 0.972 (loam), and 0.01 and 0.924 (silt loam) based on the van Genuchten model.

It could be concluded that RETC software has poor performance in the estimation of soil water curve parameters in all soil texture classes studied based on the van Genuchten and Brooks and Cory models. This trend represents the weakness of the local algorithms to solve multivariable problems where an exponential relationship exists between the variables and they are influenced by each other. On the other hand, the results show the meta-heuristic algorithms have sufficient ability to estimate parameters in multivariable problems. It could be concluded that the meta-heuristic algorithms have better performance in estimating the parameters of soil hydraulic models. The DE algorithm is the best method to estimate soil hydraulic parameters. The PSO algorithm has the nearest performance to the DE algorithm but the best performance to RETC. Finally, meta-heuristic algorithms are suitable options for estimating soil water curve parameters based on various hydraulic models.

Water is one of the most essential elements in nature that forms the basis of human life and contributes to the economic growth and development of societies. Healthy water is closely related to environmental health and activities. The life of all animals on Earth depends on water and oxygen. In addition, adequate dissolved oxygen (DO) is essential for the survival of aquatic animals. Therefore, in this study, to simulate the dissolved oxygen of the Cumberland River in the United States from the combined artificial neural network (ANN) model with wavelet and meta-heuristic algorithms of gray wolf (GWO) and bat (BA) on a monthly time scale during the statistical period. Used 2020-2010. The criteria of correlation coefficient (R2), squared mean square error (RMSE), absolute mean error (MAE) and Nash-Sutcliffe productivity coefficient (NSE) were used to evaluate and compare the performance of the models. The results showed that all three hybrid models have better results in hybrid models than the other designated models. Also, according to the evaluation criteria, it was found that among the models used in the simulation of dissolved oxygen in river water, the model of artificial neural network-wavelet with coefficient of determination (R2 = 0.958), the root mean square error (RMSE = 0.651), The mean absolute value of error (MAE = 0.334) and Nash Sutcliffe coefficient (NS = 0.962) in the validation stage showed better performance than other models.

Finding optimal pipe diameters in water supply networks for minimizing design costs due to the constraints and high decision variables is one of the major challenges for designers. In the last few decades, the development and introduction of new optimization methods, the good performance of these methods in solving complex engineering problems has led engineers to develop these methods in the design of water networks. In the present study, the performance of one of the emerging metaheuristic algorithms called Ant Lion Optimizer (ALO) in solving the problem of optimizing the diameters of water supply networks was considered. For this purpose, a multi-objective optimization-simulation model based on the ALO and the EPANET simulator model was developed. The developed model was used to optimize the diameter of Ismail Abad network pipes. The results were presented as a Pareto front between the design cost and the sum of the pressure difference squares. The results showed that using this model in addition to saving design costs about 15% compared to the existing design conditions; it has been significantly successful in reducing overload pressure in the nodes. As a result, this method can be used with confidence as an efficient method in designing water supply networks.

Uneven spatial and temporal distribution of water and population growth in the world, especially in Iran in recent decades have caused problems in providing required water resources. In this regard, knowledge of management procedures and conjunctive utilization of ground and surface water resources as one of the important aspects of integrated management of water resources and their performance in various crisis management programs, water resources conservation and watersheds integrated management seems essential. Accordingly, the present study was conducted to provide a comprehensive information of past and recent studies about the mathematical models and evaluate their efficiency in integrated management of water resources, and overall review of each model expansion, advantages and limitations. Results showed that the integrated management is one of the new and efficient strategies in recent decades. Application of optimization and simulation techniques is a powerful and useful method in determination of management strategies in development of conjunctive optimum utilization of ground and surface water resources. In this regard, MODFLOW model in groundwater flow simulation and MT3DMS model in modeling of groundwater pollution were the most used models and GMS was used as a powerful, general, comprehensive and user-friendly model. Also the literature review on the evaluation of optimization algorithms in solving problems of water resources management showed that evolutionary algorithms with various degrees of complexity are capable of effective solving complex multi-restriction optimization problems for optimum utilization of water resources, planning and sustainable development in the future. In general the provided information in this study can be applied in future planning for sustainable development of water resources.

Metaheuristic algorithms have been increasingly used in different fields. The application of these algorithms for identifying and modelling natural phenomena such as flood and drought in terms of complexity and non-linear interactions can be considered as their capability in hydrology. In this paper, Symbiotic Organism Search (SOS) algorithm was first introduced, then its application in tuning fuzzy expert system, aiming to find the region of influence area of hydrometric stations in the Southern Caspian Sea Basin. This basin has regularly experienced flood events, causing human loss and properties damages every year. The outcome of this research is used to estimate floods, and subsequently, to design flood control structures. A total of 61 hydrometric stations were selected in the study area and their physical, climatic and hydrologic characteristics including area, perimeter, minimum elevation, maximum elevation, mean slope, stream length, slope of main stream, equivalent rectangle length, equivalent rectangle width, form factor, shape coefficient, Gravelious factor, round coefficient, and mean annual precipitation were determined. Results indicated that out of 16 parameters, area, mean elevation, form factor, Gravelious factor, and mean annual rainfall, were the most significant parameters in relation to flood by employing the SOS. These variables were used as the input variables into the fuzzy system and SOS algorithm to tune the fuzzy system. Finally, the efficiency of the SOS algorithm was evaluated using the linear torque heterogeneity statistic. Therefore, 61 influence areas were determined that show homogenous areas in 61 watersheds. Results indicated the performance of SOS in determining region of influence of the sub-basins in the study area. In addition, the geographical vicinity is not a suitable criterion for finding homogenous areas.

Irrigation networks are the infrastructures in which researchers have shown an increased interest due to the huge costs of the construction and maintenance. In this sense, minimizing the costs of irrigation networks is the objective of many researchers in such a way that the conditions of minimum pressure and maximum speed are satisfied. In this study, DMPSO algorithm based on modification and mutation in PSO algorithm is proposed. Applying the proposed DMPSO algorithm, the optimum solution for the irrigation network of “Esmaeelabad” including 16 pipes and 17 nodes is explored deeply. The numeric results of hydraulic analysis were evaluated by creating a dynamic connection between MATLAB and EPANET. Using DMPSO meta-heuristic algorithm, the obtained optimum solution for the irrigation network of Esmaeelabad was compared with experimental method. It was shown that meta-heuristic algorithm reduced the total cost for 10.68% related to the experimental method and, furthermore, this proposed method is simple but more relatively accurate.

The issue of choosing the best arrangement for pipes’ diameter and optimal pump head, by considering the minimization of entire system cost, has been taken into account by hydraulic engineers over the past years. In this study, mixed integer linear programming (MILP) algorithm, as well as developed and mutated particle swarm Meta heuristic algorithm (DMPSO) is used to optimize the pressurized irrigation for a network consists of 16 tubes and 17 nodes. The objective function of the MILP includes the annual fixed cost of irrigation network diameter, and the annual cost of energy. The hydraulic constraints of the equation include the node pressure head and the flow rate constraints in the pipes. Input data includes the network map, the cost of pipes in all commercial sizes, the cost of the production facilities of pressure (such as pumps, etc.) and the limitations of the high and low hydraulic boundaries. Output is the optimal diameter of the pipes, operation pressure heads and total annual cost equivalent to the pipeline in the irrigation network. The results of hydraulic analysis with dynamic coupling between MATLAB software and EPANET have been evaluated. The optimal solution for Esmailabad irrigation network was obtained by the proposed fractional evolution particle swarm, mutated algorithm and integrated linear integer programming. Then, the experimental method was compared with the above results and it was determined that the fractional and mixed integer linear programming, have reduced the total cost of the experimental method by the value of 10.68% and 12.5%, respectively. In addition, according to the percentage of reduced values by the methods, it was found out that MILP is better than DMPSO with the least difference in network, but the DMPSO algorithm is faster with less memory response.