genetic algorithm

The aim of the present study is to optimize the cultivation pattern in the form of a multi-objective model in order to ensure the quantitative and qualitative security of crop production in Babol city. The results of the optimal model showed that based on the approach of agricultural sustainability and ensuring the quantitative and qualitative security of crop production in the study area, the amount of profit of farmers in the optimal cultivation model compared to the common model of the region increased by about 0.5% and the amount of production of crops that in the current study Its maximization was proposed as the goal of quantitative security of food production (agricultural products), it has increased by 3.8 percent. Also, the consumption of chemical fertilizers, whose minimization was included as the goal of quality security of crop production in the region, has decreased by 1.9% compared to the current state of the region. The results of the increase in the production of crops and the significant reduction in the consumption of chemical inputs confirm that the proposed cultivation model can improve the quantitative and qualitative security of the production of crops (food) in order to achieve at least one of the indicators. Food security, which is the supply of more and healthy food, should play an effective role.

The price forecasting and its changes trend is one of the most important factors in decision making and formulating strategies related to agricultural products. This study aimed at presenting a hybrid data mining model for accurate price forecasting of agricultural products, including nonlinear models of wavelet transform, genetic algorithm, deep neural network and Monte Carlo technique. This proposed model involved a two-stage hybrid model and the base model of nonlinear-nonlinear. In this proposed model, the genetic algorithm for determining the optimal lag of price time series, the wavelet function for the de-noising of price data, the deep neural network for price forecasting, the Monte Carlo method for simulating the most probable price probability and finally, the complex soft calculations for "out-of-sample forecasting with new data set" were used. Results of comparison of the proposed model including "Genetic Algorithm-Wavelet Transform-Deep Neural Network-Monte Carlo", through evaluation criteria, with three competing models of "Genetic Algorithm-Deep Neural Network-Monte Carlo", "Genetic Algorithm-Wavelet Transform-Neural Network-Monte Carlo" and "Genetic Algorithm-Neural Network-Monte Carlo" showed that the proposed model had the better performance in forecasting of future price of saffron compared to the three competing models. Also, the use of deep neural network compared to neural network, the application of wavelet theory for de-noising and also the use of Monte Carlo technique to simulate the predicted prices, increase the forecasting accuracy of future price of saffron. In addition, the use of soft calculations showed that the proposed model had the necessary efficiency and high accuracy for short-term forecasting of the future price of saffron. Therefore, the present study has a good position in achieving the index of maximum accuracy, scenario making of future price trends, sensitivity analysis of components affecting the price and finally, forecasting the future price. Accordingly, the use of the proposed model to forecast the price of agricultural products is recommended.

Saffron, as the most expensive agricultural and medicinal product in the world, has a special place in buying and selling related to the Iranian agricultural commodity bourse. The saffron commodity bourse, like any other market, has always faced limitations and structural problems, and most of these problems are related to its price fluctuations. Price forecasting using appropriate models can be a great help in reducing the price risk of futures market of saffron. The main question is that in in confronting with various price forecasting methods, which methods should be chosen to forecast the future price of saffron? The purpose of this study is to design the most appropriate hybrid model for forecasting the future price of Negin saffron in the agricultural commodity bourse, which consists of a set of nonlinear models of genetic algorithm, deep neural network, random forest, support vector machine and Monte Carlo method. In this hybrid model, genetic algorithm is used to determine the optimal lag of price time series, deep neural network, random forest model and support vector machine are used to forecast the price time series, and Monte Carlo method is used to simulate the most probable price probability. The results of this study showed that the forecasting accuracy of the hybrid model of "Genetic Algorithm-Deep Neural Network-Monte Carlo" is higher than the two models of "Genetic Algorithm-Random Forest-Monte Carlo" and "Genetic Algorithm-Support Vector Machine -Monte Carlo". Therefore, using a deep neural network and calculating the most probable price probability by the Monte Carlo method, provide the most accurate saffron price prediction with a high degree of reliability and minimal risk. Thus, it is suggested that that the management of the commodity bourse, stock market participants and researchers can use the advantages of this proposed model in forecasting the price of agricultural products.

Agriculture as one of the basic pillars of development, has an important role in economic development.  Accordingly,  using cropping pattern optimization is a proper way for agricultural development. Therefore, in the present study, optimal cropping pattern in Goharbaran region of Sari city has been evaluated in terms of multi-objective planning has been done using non-linear programming and genetic algorithm and finally compared each other.

Required data for this study has been collected with interview 250 of farmers during the 2014-2015.

Comparison The results of this study showed that the optimal pattern of non-linear genetic algorithm is superior compared to ordinary non-linear programming model. Because increasing the profit of the genetic algorithm is about 0.2% higher than normal nonlinear planning, while reduced risk the by about 6 percent. Also, the amount of production increases by about 18 percent in the genetic algorithm and the consumption of chemical fertilizer is 7 percent lower than normal nonlinear programming. Based on the results, all four sustainable farming objectives in the framework of multi-objective model in the model obtained from the genetic model have a superiority to the typical nonlinear planning model.

Since the proposed cropping pattern of genetic algorithm causes to increase farmers' gross margin compared to the ordinary nonlinear programming, therefore, the government's encouragement and support is mandatory of the farmers in applying the results of such models.

Due to factors such as political issues, technological change and natural disasters as well as the spread of environmental concerns and social responsibility, supply chain issues are no longer just a matter of looking ahead but the reverse-processing is also a matter of consideration. For this purpose, the present paper deals with the design and modeling of multi-periodic and multi-product green closed-loop supply chain in olive production. After designing the supply chain, the optimization of the whole chain profit and costs of the pollutants from the olive-product-processing process was investigated using a Genetic Algorithm based on Iranian olive data during the years 2016 to 2018. The model also came closer to reality by incorporating risk into the proposed model and taking into account the demand of all centers under risk conditions. The results show that the chain is profitable at 55417 billion Riyals, but with respect to the waste disposal to waste recycling cost-benefit ratio, 431.91, Iran's olive supply chain performs poorly in waste recycling, which by incorporating this model and revising the chain, this ratio will be reduced by 79%. Furthermore, according to the results, there is no need for import in risky conditions and all the market’s demand and export centers demand is met without any lost sales by the chain itself. It is, therefore, suggested to increase productivity in the field of olive-conversion-industries by equipping olive processing plants and constructing oil-production waste processing plants. On the other hand, in spite of the potentials and capacity of olive production and processing, it is suggested that macro-level policies be adopted to increase the consumption of olive oil in order to promote community health and create added value in order to increase the production of these products compared to its present value.

Over the last two decades, awareness of resilience and sustainability and also efforts to reduce unsustainable production patterns have significantly increased. Hence, it is crucial to examine the resilience and sustainability of production systems. Resilience explains how well production systems withstand and/or rebound from aberration. Sustainability concept based on Commission’s words is: “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. The important issue relevant to resilience and sustainability and the resilience of farms/agricultural systems is, whether resilience or sustainability can be considered as a property of a system or needs to be understood as a process. Since both of them are not essentially opposed but have various theoretical and methodological implications, it is necessary to define a resilience and sustainability indicator. So, it is required to have an intelligent objective function for fairly balancing between production systems and dimensions of sustainable production to fulfill economic benefit and the resulting environmental benefit, etc. Based on the existing published literature, studies focusing on both resilience and sustainability indicators in industrial dairy farms by using multi-objective non-linear programming and swarm intelligence algorithm have not been carried out. Therefore, it is the aim of the present study to design the “automata resilience and sustainability indicator” for industrial dairy farms. The objective function has a hierarchical structure and in order to integrate these pillars into a single score, a value between zero and one, Analytic Hierarchy Process (AHP) has been used that the value of one means complete sustainability.
Material and The objective function should be maximized which has 5 main indicators including, environmental, economic, social, technological and political issues. Each indicator has some sub-indicators. So, we designed and modeled formulas for all of them. The value of objective function is normalized, therefore, its maximum possible value is "one", which indicates the complete resilience and sustainability of dairy farms. The resilience and sustainability indicator is obtained at three levels. Eight types of constraint sets are considered. Then, the model has been implemented using data of 30[1] industrial dairy farms in Khorasan-Razavi province of Iran during 2016.   The resilience and sustainability indicator across all farms was obtained 0.43 and which was low. One of the main reasons of unsustainability and inflexibility of dairy farms under study is the unsuitable use of resources and inputs. Therefore, the proposed model (Automata Resilience and Sustainability Indicator Model) was designed and optimized. Based on result the optimum resilience and sustainability achievable for the proposed dairy farm is 0.9598 (95.98%). Thus, the proposed model succeeds in determining the dairy farms' resilience and sustainability. Furthermore, it helps in setting up other operational parameters as determining the amount of cow manure produced, the man-working hours and labor expenditure.  The obtained results should be further used as guidance for improving the resilience and sustainability of the manufacturing operation in dairy farms.   This study has introduced a formulation for a resilience and sustainability problem in process of production in the industrial dairy farm. The contribution of the proposed formulation is its ability to addresses all pillars of resilience and sustainability at the producing level. One of the main advantages of the proposed measure of resilience and sustainability is data collection that relies on data usually collected in all farms for revenue and cost analysis, cattle diet and quality control. This fact makes the model applicable to facilities introducing resilience and sustainability concepts. Thus contributes to promoting the implementation of sustainable practices in agricultural production, especially in developing countries, where still have a lack of resilience and sustainability awareness and related legislation. Using weight is important to the application of the objective function and also makes the model suitable for its intended usage in the dairy farms of developing countries. This model is applicable in the area of the optimum dairy cattle nutrition, rising profitability, reducing feed cost, decreasing GHG, managing the water and energy consumption, etc., by maximizing resilience and sustainability in dairy farms. Additionally, the results allow also for identifying the prospective measures for improving resilience and sustainability. Through results analysis, a strategy for developing resilience and sustainability can be well defined. Furthermore, the current research can be extended by integrating the model with life cycle assessment results, another producer support policies, dairy farms' capacity expansions and could also be applicable to other forms of agricultural systems by a bit changes in the decision variables and model parameters.4- This data was gathered based on non-random sampling. Because, in non-random sampling, the sample individuals are selected among individuals who have a defined characteristics and based on researcher's opinion. The proposed model is designed for a sample dairy unit. In other words, the data obtained from non-random sampling were used only to determine the status of the studied samples.

In current study, the retail price fluctuations of agricultural products including Wheat, Barley, Maize and Rice were predicted by traditional methods and genetic algorithm. Required data for all products from Farvardin 1384 to Esfand 1390 were collected. Aban 1390 to Esfand 1390 data were used for forecasting accuracy and also forecasting was done from Farvardin 1391 to Mehr 1391. In order to compare forecasting error of different methods, root mean square error criterion was used. Results showed that genetic algorithm method among all forecasting methods includes less ererr for forecasting price fluctuations of agricultural selected products, and so it, seasonal autoregressive integrated moving average (SARIMA) method is good. The root mean square error (RMSE) criterion using the genetic algorithm of Wheat, Barley, Maize and Rice price fluctuations are 80.35, 82.78, 376.23 and 923.92 respectively that forecasting price fluctuations of Wheat has the lowest error (80.35). Also, forecasting coming months showed that price fluctuations of selected crops are very high.

Electricity is an important factor in development of human societies. Regarding that and due to the role of electricity demand in making policies and decisions about production, distribution and supply of this energy carrier and also the importance of such energy as an effective input in agricultural production, it is necessary to investigate the electrical energy market and especially the demand for it. In this paper, we have used two methods, Auto-Regressive distributed lag (ARDL) and Genetic algorithms, first to estimate the electricity demand function in the agriculture sector of Isfahan province and second to analyze the main factors which affect this function. The results show that Genetic algorithms method is more accurate in estimating the electricity demand function. The results of both methods suggest that price policies has no important effects on consumption level, also the most important factor affecting electricity demand in the agriculture sector is the number of subscribers.

Several studies have estimated production function in agriculture. Most of them have used econometric methods. Recently, the heuristic algorithms have been widely in optimization problems. In this study, genetic algorithm (GA) model has been compared with a Auto regressive distributed lag (ARDL) approach to estimate the production function in agriculture. Time series data of value added, labor, energy and capital agriculture sector was used of 1978-2008. Comparing the results of two methods based on two criteria of Root Mean Square Error (RMSE) and Coefficient of Determination (R2), indicated that the genetic algorithm is more efficient than the ARDL approach