genetic algorithm

The aim of this study is to explain to the theoretical model in order to find a new solution for the production and development of spatial arrangement of architectural plans based on interactive and integrated methods with the help of machine learning and genetic algorithms. Evolutionary algorithms alone are not effective, but machine learning algorithms can learn plans and form the basis of practical models that can develop and generate new samples through the use of genetic algorithms.

In this regard, the combined research method includes library studies, collecting raw data, reviewing case samples, and using computational formulas as objective and penalty functions.

Studies show that the genetic algorithm does not have the ability to store memory and on the other hand, the basis of its calculations is jumping and random action that this process is not effective in the production of architectural plans alone and research.

 findings show that the algorithm Machine learning, due to its exemplary structure, can store and recognize examples, and the genetic algorithm, which is a searchable and scalable algorithm, can produce more examples of architectural plans each time based on the proposed mathematical model.

In this study the adsorption of the anionic dye, aniline blue, onto a novel biosorbent with the local name of Tonbak Toureh and the scientific name of Grantia aucheri was considered. FT-IR and SEM were used for characterization of the biosorbent. The effect of dominant parameters including pH, amount of biosorbent, time, temperature, and initial dye concentration were investigated and the optimum conditions of pH of 4-5, time of 10 min, temperature of 25 ͦ C, dye concentration of 40 mg L-1, and biosorbent amount of 12 mg were achieved. The adsorption isotherms corroborate the experimental data were appropriately fitted to the Langmuir model with high adsorption capacity of 65.79 mg g -1. Artificial neutral network-genetic algorithm (ANN-GA) was applied for prediction and optimization of the process in which the closeness of the predicted values with experimental data confirms the efficiency and potential of ANN-GA for prediction and optimization of the adsorption process. The thermodynamic parameters were assessed. The positive ΔH° and ΔS° values described endothermic nature of adsorption. The adsorption of aniline blue followed the pseudo-second order kinetic model.

Saving on inputs, preservation of fossil fuels and decreasing air pollution are the advantages to using energy efficiently.  The purpose of this study was to optimize energy inputs and greenhouse gas emissions of orange product in Dezful County, with two methods of data envelopment analysis and multi-objective genetic algorithm optimization techniques.

 Data from 60 farmers were randomly collected through face-to-face interviews and a questionnaire during the year 2016 and using two methods of data envelopment analysis and multi-objective genetic algorithm were analyzed.

Regarding the results of data envelopment analysis technique based on fixed and variable return scale models and input-axis measurement, technical efficiency, net technical efficiency and scale efficiency were calculated to be 0.95, 0.98 and 97%. The results of optimization of energy consumption and greenhouse gas emissions by using data envelopment analysis showed that about 4.36% of orange energy has storage potential, that chemical fertilizers and diesel fuel have the highest amount of stored energy of all stored energy. Data envelopment analysis can reduce 34.38 kg of carbon dioxide per hectare from greenhouse gas emissions in orange crops. The results of the genetic algorithm showed that if all inputs were completely optimized, it could reduce the energy consumption of orange production in the study area by 26.1%. Also, the energy input for ideal orange production system could be 32810.6 MJ per ha.

According to the results of this study, it is recommended to use of new machines and encourage farmers to conduct soil tests before using of fertilizer.

In recent years, a variety of models have been used to evaluate the waste management systems in Tehran in order to select the best waste management options. But the problem of the final disposal of waste in Tehran is still one of the issues related to the environmental management of the metropolis. The purpose of this study is to provide one model for allocating optimal annual quantities of waste to waste management subsystems of Tehran in order to achieve the highest efficiency, reduce costs and increase system revenue.

In this research, first by referring to the Arad Kooh complex in Tehran and interviewing with experts and using the information recorded in this complex, the required data was collected. Then, an optimal model proposed for allocating optimal annual amount of municipal waste with considering all of limitations to 5 sub-systems of recycling, aerobic compost, anaerobic digestion, incinerator , landfill using genetic algorithm improved by fuzzy logic with the aim of reducing the total cost of the municipal waste management system in the MATLAB environment and its results were analyzed.

The significant results showed with increasing capacity the subsystems with lower cost and more profitability, the system will not necessarily seek to be optimized and optimal amount of waste allocated to each of the subsystems such as recycling, aerobic compost, anaerobic digestion, incinerator and landfill were estimated about 750,000, 960000, 182000, 325000, 780000 tons in each year, respectively.

According to the results of the optimal model proposed in this study, it is necessary to carefully follow the flow and optimal allocation of waste from the annual production of Tehran to each of the following subsystems: recycling, aerobic compost, anaerobic digestion, incinerator and landfill in order to achieve the high annual efficiency for municipal solid waste management system in Tehran city.

Migration to cities and urban development have led to the irregular growth of cities and the uncontrolled transformation of natural land cover into artificial and impenetrable cover. As a result, it has created numerous environmental consequences for cities, including the phenomenon of heat islands, as a result of which the temperature of urban areas has increased compared to the surrounding areas, causing changes in ambient temperature, air pollution and harmful effects such as greenhouse emissions. Therefore, measuring and controlling the effects of urban heat islands, based on scientific and justifiable principles, helps decision-makers to overcome the resulting problems. Today, one of the most effective ways to control the effects of heat islands in developed countries is to use less heat-absorbing coverings, such as green infrastructure, high-albedo materials and flagstone, to cover the roofs of buildings. Therefore, in this study, an optimal planning based on spatial analysis, using the remote sensing and computational intelligence in the form of a spatial decision support system that can determine the effects of changing the roof covering of buildings in the study area.

To survey the research, a neighborhood from a central region of Tehran, the 7th region, was chosen to develop a software package for green roof planning. It is expected that the UHI effect has a significant role in this neighborhood since the region that the neighborhood belongs to, is one of the central regions in Tehran. Moreover, for developing the software package, map of parcels with attributes related to the area and land use and Images of Landsat 8 over the neighborhood are employed. Two main groups have a pivotal role in calculating UHI indices including vegetation and urban groups. When the indices are developed, the relationship between UHI and indices is investigated using the linear regression method (LRM) to obtain indices’ coefficients. Afterward, the software package tries to find some parcels, which constitute a certain and predefined percentage of area, that have a significant impact on UHI’s standard deviation by changing their roofs’ covers into three types cover including green, high albedo materials, and flagstones as the novelty of the research. Since there are a lot of feasible solutions, it is necessary to use a metaheuristic algorithm for finding the optimal solution. Therefore, in the second step of the proposed method, the optimal solution is conveyed by the Genetic algorithm (GA), as the most common algorithm in metaheuristic algorithms. After finding the optimum parcels for changing roofs’ cover, the UHI effect is computed once again to show the improvements.

As mentioned, the Genetic Algorithm is used to select the optimum subset of parcels for changing their roofs’ infrastructure with three covering classes including vegetation, high-albedo materials, and flagstones. This subset is assumed as 10 percent of all parcels in the area. However, some parameters should be set before using the algorithm such as the number of population and generation, the ratio of selection, crossover, and mutation. Besides, minimizing the standard deviation of SHI values was assumed as a fitness function for GA. As a result of the algorithm, the selected parcels and their appropriate roofs’ infrastructure for minimizing the standard deviation of SHI in the area are presented. This optimal solution was obtained through 252 generations that its convergence trend is presented. Additionally, based on the changes of selected parcels for roofs’ cover, the SHI values for the study area are computed again. These new values for the SHI and UHI effects are presented. However, the obtained standard deviation of SHI values for the changed roofs’ cover is 10.781°C while this value before changes is 13.222°C.
By examining the selected parcels obtained from the GA results with green spaces in and around the study area, it is found that the GA selects parcels for changing the roof covering with vegetation that is not contiguous with the green spaces in their surrounding area. whereas, according to results, the GA did not choose any parcel in these areas to change their roofs’ infrastructure to vegetation cover. However, highly efficient covering in SHI values such as vegetation and high albedo materials circumscribed the study area. This fact shows that in order to control the variation of UHI in the center of the area, it is necessary to curb the SHI values in the border of the study area. However, the less efficient cover compared to vegetation and high-albedo materials, which is flagstones, are located dominantly in the center of the study area since their influence is more limited and local than the other types.
It is also can be perceived that all changes in roofs’ infrastructure are not in line with changing to the vegetation cover, although this type of covering has the best effect to reduce SHI value. This consequence is because of the fitness function of GA, which is based on the standard deviation and not the mean value. The type of vegetation for covering decreases the SHI value, and thereby leads to decreasing mean value, while the objective of the software is to minimize the variation of SHI values. Therefore, vegetation cover is used in a location where the study area confronts with hotspot SHI value at that location. To verify this claim, the vegetation cover is utilized for all parcels selected by the GA to compute the SHI value for this scenario

With the widespread growth of cities and the increase in population, natural covers have been changed to artificial and impenetrable land cover, which lead to several environmental problems for cities, including UHI effects. Due to these changes, which are caused by the UHI effects, the temperature of urban areas becomes higher than the surrounding areas. One of the most practical and efficient methods for controlling the effects of urban heat islands is utilizing the green infrastructure and high albedo materials for roofs’ infrastructures; however, previous studies did not model this subject in quantitative practice. Based on this shortcoming, the present study proposed a software package to investigate quantitatively the changes of UHI effects based on the substitution of present roofs’ infrastructures to three selected types of covering class including vegetation, high-albedo materials, and flagstones. Additionally, the software used GA as a sub-model of the software to select the best set of parcels in the study area for changing their roofs’ infrastructure according to a specified fitness function. The fitness function assumed for this research is the standard deviation of the SHI values in the study area. This fitness function controls the variation of the SHI values and prevents the creation of UHI hotspots in the study area. . Examining the selected parcels obtained from the results of the GA with the surrounding green space and the study area, it was found that the genetic algorithm selects parcels to change the roof covering with vegetation that is not adjacent to the green space. This fact shows that in order to control the change of UHI in the center of the region, it is necessary to limit these values at the border of the region. However, less efficient cover compared to vegetation and high albedo materials, which is flagstone, is predominantly studied in the center of the area. It can be also seen that not all changes in roof infrastructure are consistent with changes in vegetation, although this type of cover has the best effect on reducing the amount of urban heat islands. This result is due to the fitness function of the genetic algorithm, which is based on minimizing the standard deviation of SHI in the area. Therefore, the vegetation is used in a place where the study area is exposed to a high amount of urban heat islands. Additionally, this cover type is more effective in the range of 100 and 150 meters of green areas.

A spatial distribution and accuracy of groundwater quality data is required for management of groundwater resources. These data are usually collected from monitoring wells which are spatially distributed in the studied aquifer. In the design of the monitoring network, the minimum number of monitoring wells with an optimum spatial distribution is necessary to ensure a cost efficiency. Therefore, the configuration of the wells distribution and their number in groundwater monitoring networks are an important problem for optimizing groundwater issues. This study was targeted to find an optimal monitoring network with minimum number of wells in the Guilan’s aquifer so that provides sufficient spatial distribution on groundwater quality. Salinity is one of the most important criteria for the quality of groundwater which is measured by using of parameters such as Total Soluble Solids (TDS), Chloride ion (Cl) and Electrical Conductivity (EC). Hence, EC was selected as a quality parameter in the design of the monitoring network in this study.

Genetic optimization algorithm (GA) was used to search for optimal quality monitoring network. In this method, a possible network of monitoring wells located in the aquifer considered for each “chromosome”. Then each monitoring well in this network is represented by a binary bit. Finally, they are coded by bit value equals to 1 for well that was selected for the network or by bit value equals to 0 for well that was not selected for the network. In this paper, two conflicting objective functions are simultaneously solved. The first objective function is the maximization of the match between the interpolated EC distributions obtained from data of the all available monitoring wells and the wells from the newly generated network. The match is evaluated by using of the Nash-Sutcliffe (NS) model efficiency. The second objective is the minimization of the number of the monitoring wells in the newly generated network by considering cost-related constraints. These two objectives are integrated in a single objective function where different combinations of both objectives are investigated by considering two cases.

The results showed that the relative importance of each objective is expressed using the weighting coefficient, w. It was found that the solution of the optimization is very dependent on the selection of w. Therefore, a w value that are resulted by the most balanced solution should be selected. Most balanced means that the trade-off between cost and spatial distribution is most acceptable. To choose the most solution, it is highly recommended to evaluate additional performance indicators besides NS coefficient such as RMSE, PBIAS, the regression coefficient and standard deviation. Additionally, mean values of EC observed in the optimized network are higher than those in all monitoring wells. Therefore, it could be clearly concluded that the optimized network provides groundwater quality data from more polluted areas.

The results showed that the optimization approach significantly reduces the number of monitoring wells with spatial distribution of the EC values. Additionally, the monitoring network was optimized such a way that sampling points were removed from less polluted areas and were selected in areas with higher pollutant concentrations. The optimal design of the monitoring network should be performed periodically. Since monitoring efficiency is expected to change when the data of the new wells become available, a re-evaluation of the optimized monitoring network considering the addition of new wells every few years may help the determination of the long-term effectiveness of a groundwater quality monitoring program.

Sustainable development necessitates the supply of energy resources in a sustainable manner, with a reasonable cost and with minimum negative social and environmental impacts. Thus the optimization of energy consumption, and as a result, the reduction of environmental emissions is of particular importance. The purpose of this study was to assess the amounts of consumed energy and pollutant emissions, optimization of energy consumption, and reduction in environmental emissions in the cake production industry in Guilan Province using data envelopment analysis (DEA) and genetic algorithm (GA).

The efficient and inefficient units considering energy consumption were identified using DEA models. The optimal energy consumption pattern based on efficient units was presented for other cake production units, and the environmental assessment was performed based on the optimal pattern. Finally, using the multi-objective genetic algorithm (MOGA) and considering two objective functions aiming at increasing the yield and reducing the global warming (GW) index, the optimal energy consumption pattern in cake production units was presented.

The results of this study showed that 260532.25 MJ of energy was consumed for a daily production of 4157.14 kg of cake. The highest share of energy consumption was allocated to natural gas with 128582.1 MJ. Also, GW index was calculated 13099.49 kg CO2 eq. per ton of produced cake. According to DEA results, from a total of 21 cake production units, 17 units were recognized efficient based on variable returns to scale model. Based on DEA results, the total energy consumption for optimum consumption of inputs, the energy saving percentage, and the reduction of GW index were determined 254929.28 MJ day-1, 2.15%, and 550.18 kg CO2 eq. per ton of produced cake, respectively. Also, the energy use pattern proposed by the MOGA resulted in 36.3% reduction of energy consumption, in which the highest percentage of energy savings was associated with human labor. Based on the optimization results of MOGA, GW index for
production of one ton of cake was calculated 10038.44 kg CO2 eq.

MOGA optimization method in comparison to DEA, resulted in more reduction of energy consumption, GW index, production costs, and environmental burdens as well as higher income. Thus, the use of MOGA will pave the way for achieving sustainable development in cake production industry and staying in competition with other food industries.

Diameter, insertion depth and spacing of drainage pipes are three crucial variables in the design of underground drainage network. Effluents have a great potential to leave lots of damage on the environment. The proper selection of design variables can lead to reducing the environmental damage. The purpose of this paper is to provide a model for selecting optimal design parameters for underground drainage systems to reduce environmental damage, in a way that after the discharge of drainage to the river, river salinity concentration does not exceed the acceptable limit.

For this purpose, maximization of difference between drainage water salinity and acceptable limit was considered as the objective function. Genetic Algorithm (GA), kind of evolutionary algorithm, has been used to simulate the transmission and the salt Hooghoudt model was used also. In Hooghoudt model water transition to drainage is modeled in two upper and lower individual part. In order to evaluate the proposed model, an agro-industrial unit Salman Farsi was chosen as case study. Matlab software was employed to program the formula and algorithm which has been used in this research, including Hooghoudt salinity transfer simulation function and GA algorithm optimization.

Results show that the pipe depth is complying with minimum allowable depth. In other words, since the objective function of the model is to achieve minimal environmental damage, the minimum depth of installation is generally chosen. Optimum diameter, insertion depth and spacing have been obtained 1.3, 0.1 and 34.3 respectively. The results of the sensitivity of the model to change of the two basic assumptions, minimum allowable depth and stabilize the water table depth stabilizing, shows by increasing the allowable minimum depth of installation, drainage spacing increases and reducing the depth of the water table stabilizing will increase the drainage intervals and leads to increasing the concentration of drainage water discharged to the environment.
Discussion and

In this study and by using information about the Salman Farsi agro-industry company, to reduce the environmental damage caused by drainage projects, installation depth of drainage should be equal to the minimum allowable depth.

Limitation of surface water resources and excessive water utilization from aquifers, as well as the pollutants intrusion through the agricultural, urban and industrial activities, cause irreparable damage to groundwater. Groundwater system doesn’t respond quickly to contaminants and contaminants reaching time to the groundwater and its release into the aquifer is usually long. Remediation of contaminated groundwater and re-use often takes a lot of time and money and sometimes finding an alternative source of water is not possible. Therefore, the best and most effective solution is preventing the contamination entry to this valuable source. Determining the groundwater contamination level is one of the most important hydrogeological studies, which in this regard, the identification of susceptible areas and aquifer vulnerability assessment has great importance. Determining groundwater contamination is one of the most important hydrogeological studies. In this regard, identification of the contamination risk areas the aquifer vulnerability assessment has great importance. The Miandoab study area as a most important plain of the Urmia lake basin is one of the agricultural areas in Iran, especially in grape production, therefore, because of overuse of various chemical and animal fertilizers, it can be a nitrate-contaminated plain. Therefore, considering the importance of groundwater in the Miandoab region, which is also used for drinking, the vulnerability assessment of this plain is necessary. In the present study, the study of contaminant risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. 2. Materials and Methods The Miandoab Plain with an area of approximately 1150 Km2 is located in the south of Urmia Lake and is a part of the Alborz-Azarbayjan structural zone from the geological viewpoint. The average annual rainfall, based on the thirty years (1989-2018) data from Malekan and Miandoab synoptic stations is about 284 mm per year. This region, based on empirical Emberger method (1952) and using the statistics meteorology data, has a cold and semi-arid climate. Figure 1 shows the location of the study area. Fig 1. Geographic location of the study area The DRASTIC model has been used to mapping the groundwater vulnerability to pollution in many areas. Since this method is used in different places without any changes, it cannot consider the effects of pollution type and characteristics. Therefore, the method needs to be calibrated and corrected for specific aquifer and pollution. The DRASTIC model was improved with several methods such as the artificial neural network. In this study, the genetic algorithm is proposed for groundwater vulnerability.

The DRASTIC map was created by applying the weights for each parameter and integrating and overlaying the layers. According to the results of the DRASTIC model for plain, 15, 10, 17, 33 and 25 percent of the plain, respectively located in areas with very low, moderate, high and very high vulnerability. In the genetic algorithm method, the optimal weights of the parameters were obtained by maximizing the objective function. Based on the genetic algorithm method, groundwater depth, hydraulic conductivity, and unsaturated medium have the most effect on the vulnerability of groundwater in the region, respectively. Fig 2 shows the vulnerability map of Miandoab plain based on DRASTIC and optimized DRASTIC. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. A) B) Fig 2. Vulnerability map using: A) General DRASTIC, B) optimized DRASTIC using genetic algorithm

In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.

In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.

In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.