genetic algorithm

The location problems of repair centers in the railway network have been developed to quickly and cheaply solve the failure of signaling equipment. The signaling system in railway stations guarantees the safety and smooth running of passenger and freight trains. Failure of signaling equipment is inevitable, which leads to disruption of train movement and dissatisfaction in the customer community (passengers and goods owners). Hence, it is important to quickly fix equipment failure in priority stations. The signaling equipment of each station includes interlocking, track circuit, machine needle, signal light, and axle counter. This equipment has an average monthly failure frequency, average repair time, and different thresholds for fixing the failure. This study investigates the disturbance caused by the failure of signaling equipment with the importance of stations and the average frequency of equipment failure. Importance indices were extracted to prioritize the stations. The weighting of the indicators was done using the fuzzy hierarchical analysis method. Then, the stations were prioritized based on their importance using the TOPSIS multi-criteria decision-making method. In the following, a multi-objective optimization model of location and allocation is developed to maximize the coverage of the entire maintenance network and reduce the time and cost of troubleshooting. To solve the model, the method of genetic algorithm of non-dominant sorting is used. The data of signaling equipment failure in Mashhad-Tehran railway stations is analyzed as a case study and the importance of stations and demand points is calculated. Comparing the output results with the current status of the repair centers in this study shows that the new location of the repair centers and how the stations are allocated improves the coverage number of the priority stations.

One of the most significant aspects of train timetable optimization is determining the arrival dwell time in the subway system. A good timetable improves the efficiency of trains and resources, reducing passenger wait times. This study provides a timetable optimization model to reduce passenger journey time. In this model, train movement between two stations is divided into three phases: accelerating, coasting, and breaking, with random numbers used for running duration in each phase due to stochastic delays in crowded stations. First, we develop a stochastic integer programming model that includes headway and dwell time. The Scenario-Based Uncertain Programming Approach is then utilized to simplify. Finally, numerical examples are provided for Tehran's metro system. The model's optimal solution is determined via a genetic algorithm. The results demonstrate that the model can lower the passenger waiting time when compared to the current timetable.

Efficient transportation of goods is crucial for cost reduction, improved delivery time, and enhanced service quality. Advanced logistics systems analyze data to find the most efficient routes. This minimizes fuel consumption and decreases transportation costs. The Vehicle Routing Problem with Time Window Constraints (VRPTW) is a classic optimization problem in the field of operations research and logistics. It is a challenging optimization problem in logistics, classified as NP-hard. Hybrid approaches combine multiple optimization techniques to improve the quality and efficiency of solutions. This paper presents a hybrid cat-swarming algorithm that utilizes genetic operators to effectively address the VRPTW problem. The goal is to determine the optimal routes for the vehicles, considering both the vehicle capacity constraints and the time window constraints at each customer location. In this paper the objective function of the algorithm aims to minimize both the total distance traveled and the number of vehicles utilized, ensuring efficient and cost-effective routing. The hybrid cat swarming algorithm proposed in this study offers a novel approach to tackle the challenges posed by the VRPTW problem. By integrating genetic operators such as crossover and mutation, the algorithm enhances performance and improves the quality of solutions. Its primary objective of minimizing total distance and vehicle usage guarantees efficient and economically viable routing strategies. To evaluate the effectiveness of the algorithm, it was tested using a simulated dataset of salmon samples as a benchmark. For samples comprising 50 customers, an improvement of up to 48 to 59 percent in previous response rates has been achieved. For samples comprising 100 customers, optimal global responses, as obtained from previous articles, have been observed in several instances. The proposed algorithm is suitable for transportation and logistics systems with limited customers and leads to cost reduction, improved delivery times, and increased service quality.

Today, one of the most important concerns of urban planning managers is achieving sustainable urban development. An important point in the discussion of providing and creating financial resources for municipalities is the stability of income sources. Considering the importance of the city of Karaj due to its proximity to Tehran and unfinished urban construction projects that are facing problems due to the lack of adequate financial resources in the construction and operation stages, considering what was said, a model should be sought to optimize sustainable incomes. Municipality aims to manage the construction of urban construction projects, which has been used due to the advantages of the genetic algorithm. In this research, after collecting the income information of Karaj Municipality for the last 11 years, we optimized the studied resources by using the genetic algorithm and Markowitz's portfolio method.By comparing the estimated weights with the existing weights, it can be said that the income structure of Karaj municipality has not had a proper balance. Currently, a large part of Karaj Municipality's income portfolio is allocated to income codes 6 (subsidies and gifts and assets) and 1 (income from general taxes and continuous incomes), which shows that a high share of Karaj Municipality's income is dependent on unstable incomes. . Based on the results of the research, it is suggested to increase the share of the price of services and incomes of municipal for-profit institutions (code 3) and incomes from municipal funds and properties (code 4).

The optimization of truss structures, including the optimization of topology, shape and cross-sectional area (size) of truss members, has been the focus of various researchers over the past years. The aim of this study is to optimize the discrete and continuous size of two-dimensional trusses with fixed topology and shape. For this purpose, the cross-sectional area of all the members is selected as the design variables and minimizing the weight of the structure as the objective function, and the constraints of the problem include the restrictions related to the changes in the location of the nodes and the tension in the members, the permissible values of which are determined using the conditions of the problem. In this study, genetic algorithm is used to optimize the truss. OpenSees structural analysis software is used to analyze the structure and obtain member forces and displacement of nodes. This software is properly connected with genetic algorithm code and particle swarm algorithm prepared in MATLAB software. In this research, the optimization of 2 two-dimensional trusses, including a six-node ten-member truss and an eight-node fifteen-member truss, is investigated, and the results of this research are compared with the results of previous articles. The results of the comparison show that in discrete sizes, the plans obtained from the genetic algorithm are generally more economical than other plans.

Guyed masts are slender, tall structures widely used to support various types of antennas in the field of telecommunications. These structures consist of a tall vertical mast laterally supported at several levels along its height by sets of inclined pre-tensioned cables. It is well known that natural frequencies are fundamental parameters that affect the dynamic behavior of structures. Optimizing natural frequencies of Guyed masts is an important issue in structural engineering. The main objective of this study presented in this paper is to obtain a design that will maximize the frequency of the Guyed masts. Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, three population-based meta-heuristic algorithms are employed for frequency optimization of Guyed masts. These algorithms consist of the Genetic Algorithm (GA), Firefly Algorithm (FA) and Particle Swarm Optimization Algorithm (PSO) aims to maximize the natural frequencies of Guyed masts while satisfying their constraints. The study on optimizing the position of the cable anchorages along the pole of a guyed tower using algorithms demonstrates that the optimal values for these angles are approximately 35 degrees without pretension forces in the guys. Additionally, the optimum cable angle is 75 degrees when subjected to prestressed guy cables.

The main goal of this article is to determine the most appropriate time for field observations in pavement management by using the algorithm. It will be innovative to prevent more costs and have better planning for pavement maintenance and repair so that the roads always have an acceptable level of service. In addition, in this topic, the financial issues related to field observations should not be neglected; But these costs are very small compared to the huge costs spent on roads; However, the indicators affecting the cost of field harvests should also be specified and these costs should be optimized along with determining the best time for conducting the inspection. Therefore, this article tries to determine the most appropriate time for field surveys, so that with the first surveys, relevant modeling, necessary planning, financial estimates, etc. can be done and steps can be taken to optimize inspections of asphalt pavements. and in the meantime, traffic and financial issues are also in mind. For this purpose, according to the effective factors and with the help of genetic algorithm, the required program was implemented in MATLAB software and tested for different samples from Khuzestan province, the results of which are very impressive and reasonable. By giving the age of the pavement and the coefficient of importance of the pavement, the genetic algorithm starts working and optimizes the traffic, temperature, PCI and quality factor to finally obtain the optimal time of field harvests.

Concrete, as one of the most widely used building materials in various constructions, is essential in providing the security of structures, especially hydraulic channels, in terms of resistance against water and chemical penetration. In this research, effect of zinc oxide nanoparticles on permeability of concrete as well as its mechanical characteristics is investigated experimentally and in a laboratory. By performing unidirectional compressive tests, the compression and tensile strength of concrete containing values of 0%, 0.1%, 0.5%, 1.0% and 1.5% at the ages of 7 and 28 days have been determined. Also, permeability and water absorption rate were also checked. According to the test results, the mechanical resistance of concrete increases with the increase of nanoparticles to a certain extent. Also, in the best case, per 0.1% amount of zinc oxide nanoparticles, the permeability has decreased by 97% compared to the control sample. This is because nanomaterials improve mechanical resistance by creating a denser and less porous structure in the mixture of mortar and concrete. Analytical models were developed using the genetic algorithm to describe the time-dependent characteristics of concrete samples mixed with nanoparticles in different compressive and tensile states at different ages. Therefore, in this research, an attempt has been made to predict the mixing plan of concrete containing nanoparticles by using neural networks along with the genetic algorithm method. The purpose of this modeling is to show the accuracy of neural networks in predicting the compressive, tensile, and permeability resistance of concrete with different percentages of zinc oxide nanoparticles.

This paper investigates the accuracy and convergence rate of different metaheuristic algorithms in determining the stiffness of structural elements using structural modal parameters and defining a suitable objective function. To achieve this purpose, three different structures, including a three-story one-dimensional frame, a six-story one-dimensional frame and a two-dimensional truss, were investigated. The metaheuristic algorithms, employed in this study, were Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization. The objective function utilized in this study consists of two terms; the first part involves the squared difference between the first frequency of the structure obtained from the responses of the investigated structure and the first frequency obtained from the hypothetical stiffness matrix in each generation of algorithms. The second part measures the norm of the difference between the first mode shape of the structure obtained from the responses of the investigated structure and the first mode shape obtained from the hypothetical stiffness matrix in each generation of algorithms. By minimizing the objective function, the Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization determined the element stiffness of the three-story, six-story and truss structures, thus demonstrating the high efficiency of metaheuristic algorithms in resolving unknown parameters of structures. The average run time for the Genetic Algorithm was 3.38 seconds, 4.47 seconds, and 15.73 seconds for the three respective problems. For Particle Swarm Optimization, the times were 3.76 seconds, 6.47 seconds, and 16.76 seconds. The Teaching–learning-based Optimization achieved times of 1.92 seconds, 4.51 seconds, and 12.76 seconds. The Teaching–learning-based Optimization exhibited the highest convergence rate and the lowest error compared to the Genetic Algorithm and Particle Swarm Optimization. For example, in the two-dimensional truss, the values of the objective function in the last iteration of the Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization were , and , respectively. The Particle Swarm Optimization demonstrated an acceptable convergence rate and error compared to the Genetic algorithm. The Genetic Algorithm, however, displayed a significant error rate in determining the stiffness of structural elements compared to the other two algorithms.

In this research, the relation between soil spectral reflectance using Landsat 8 satellite data as well as SRTM Elevation data and soil organic carbon has been investigated. In the proposed method, spectral reflection of data in the main bands of Landsat 8 satellite is investigated and processed. In addition to the main bands, vegetation and lighting indices and topographic features have been studied. In this study, we try to select the effective indicators in increasing the accuracy of soil organic carbon modeling. For this purpose, in the first step, the carbon model was performed using linear regression, support vector machine regression and neural network methods. In order to implement the proposed method, 100 soil samples in East Azerbaijan province have been used. According to RMSE and R2 statistical indices, which are the basis for evaluating the models, the neural network model was selected as the final model and with the values of RMSE = 0.0798, R2= 0.8741 and RRMSE=9.5683 is more accurate than the regression method. Due to the importance of dimensionality in order to increase accuracy and reduce the complexity of calculations, a genetic algorithm was proposed in this study. This efficient algorithm increases the accuracy of soil organic carbon modeling and eliminates additional indicators. After applying the genetic algorithm on the neural network model, we were able to achieve better accuracy and the values of the baseline statistical indices were changed to RMSE = 0.043, R2 = 0.9398 and RRMSE=5.1559.

Lateral intake and stream diversion facilities, which are widely used in irrigation, land drainage and municipal sewage systems, deal with sediment transport. In this research, using a numerical discrete phase model, simulation of the sediment transport phenomenon is carried out in a 180-degree channel bend with a lateral intake that is located at a position of 115 degrees outside the arc and with a 45-degree diversion angle. The numerical model was calibrated with laboratory data. Then, in 31 positions of the channel bend from the 10 to 140 degrees with 5 degrees' intervals and with 5 diversion angles of 10, 30, 50, 70 and 90 degrees, and for three diversion discharge ratios of 20%, 30% and 40%, the calibrated numerical model has been implemented and the percentage of sediment entered to the lateral intake were determined for each model. The results were used as the data necessary for training and validation of Artificial neural network (ANN) model. The inputs of ANN model were location of lateral intake in outer bank of channel bend (φ) and diversion angle of lateral intake (θ), diverted water discharge ratio (Qr). The output is diverted sediment ratio into the lateral intake (Gr). The data were used for training and validation of the ANN model and the best structure was obtained for the neural network models using R2, SSE and MARE criteria and compared to regression models. The best structure of ANN model was obtained as a 5-layer model with 3 membership functions for each input variable and 27 conditional rules. The accuracy of the results obtained from the models in terms of the mean absolute relative error (MARE) indicates the ability of the neural network models to predict the amount of sediment diverted to the lateral intake with respect to regression models. Then, by developing a new simulation-optimization method based on the Multi-Objective Genetic Algorithm Optimization model (NSGAII), the FLUENT numerical model and neural network models, the optimal position and diversion angle of the lateral intake in the U-shaped channel is determined to minimize the amount of sediment inflow into the lateral basin and maximize the rate of dewatering discharge. By connecting the best model obtained from the ANN model, to NSGA-II model, the Pareto Front contains a list of optimal positions and optimal diversion angle of lateral intake are obtained with respect to the objective function and its limitations in the U-shaped channel. Finally, to determine the optimal position and angle of the lateral basin in the U-shaped channel by connecting the Pareto front obtained from the Multipurpose Genetic Algorithm (NSGA-II) to the TOPSIS multi-criteria decision-making method, the best option is found among the Pareto front options. The results include the optimum position and optimum angle of lateral intake dewatering along with diversion discharge and sediment rates corresponding to these values. The results show that the optimum diversion angle obtained is 38-degree and the optimum position obtained is 127-degree

The goal of any structural design process is to produce a safe design that meets all the design codes requirements, while trying to minimize the cost of the design. In Civil Engineering Structures, majorly cost is the objective of optimization of the structure. In most cases, the material cost is included in objective function. In some cases, construction cost is also included in objective function. The objective of the optimization is to minimize the cost of the Structures while satisfying strength and serviceability constraints. In order to investigate the efficiency and accuracy of the modern optimization methods in comparison with classical methods, an elevated INTZE tank is primarily designed based on the recommendations of Indian code and then optimized using Genetic Algorithm (GA), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO). objective function is the material cost of the tank which is the function of the design variables. The total material cost of the tank can be expressed as the sum of the cost of concrete, reinforcement and cost of formwork. Design constraints of the problem also include the stresses of the structure members and the limitations of the volume and geometric dimensions of the water tank. All constraints were satisfied and the stresses and stability constraints were in acceptable ranges.

The postal network is a network in which the flow of shipments is collected by vehicles from several sources and transferred to hubs; In the last step, the parcels are sent to the final destination by the vehicles. In order to provide this service, the hubs are located to process the parcels and the local routes are planned to pick up and delivery the packages in the hubs. This problem is known as location-routing problem. The current research seeks to locate the hub and the routing of freight carriers on an intercity scale and at the level of Iran. For this purpose, first, the mathematical model of the problem of locating the hubs and routing the cargo carriers is presented, and since the problem of locating-routing is in the category of complex problems with large dimensions, meta-heuristic algorithms have been used to solve it. In previous studies, meta-heuristic algorithms or a combination of them have been used to solve the location-routing problem, but in the study, the location-routing problem has been solved by integrating two genetic algorithms (GA) and the particle swarm optimization algorithm (PSO). The results of this research show that the integration of two algorithms, PSO and GA, provides a better answer than if only one meta-heuristic algorithm is used to solve the problem. It is worth mentioning that in the current research, the data of Tipax Company was used to locate the hubs and local tours routing.

Texture quantization is a useful method for extracting spatial relevance between pixels, which is used in the human brain for image interpretation. Aside from spectral bands, textural features of high spatial resolution image can be used to improve classification accuracy. Finding proper textural features among available features is important for special case studies.

In this paper, two methods based on genetic algorithm (GA) are introduced to choose efficient features. The first is binary GA, which improves classification accuracies through selecting the best textural features. The second one is GA with a variable number of selected features in a refined and full feature space. Results show that the best combination does not necessarily consist of features with improved individual accuracy.

The proposed methods have better accuracy, less number of features, and less computational time when comparing with the simple GA. They could be used based on the number of spectral bands, number of generated features, and train and check pixel number. Second method needs more prerequisite time and could be used for images with fewer bands, train and check pixels, and generated features, because increasing these items increase computational time very much. Second method could be used in large images with more train and check pixels but led to more selected features.

Results obtained on three datasets indicate 7.7 to 50.48 percent improvement in mean accuracy.

In this paper, a new and efficient performance-based method is introduced for the optimal design of the tuned mass damper (TMD) to control the response of nonlinear structures. In this method, the FEMA-P58 probabilistic evaluation framework is used in the design and evaluation of TMD performance in order to reduce the repair cost and time caused by structural and non-structural damages. For this purpose, an innovative objective function has been defined based on structural responses resulting from different earthquakes, which is compatible with the probability of the repair cost and time exceeding a certain value in the FEMA-P58 method. Uncertainties due to earthquake records are directly considered in the introduced objective function. The genetic algorithm is used for the optimal design of TMD based on the proposed objective function. Also, due to the fact that considering the uncertainties in the input earthquake records leads to increasing the calculation time, therefore the artificial neural network technique is used as a fast estimator of the nonlinear dynamic response of the structure to reduce the calculation time. The probabilistic evaluation of the performance of the structure equipped with the proposed TMD shows the efficiency and effectiveness of the proposed design procedure in reducing the expected repair cost and time. So that the expected repair cost and time in the structure equipped with the proposed TMD under the design earthquake have been reduced by about 29% compared to the uncontrolled structure.

The Complexities of Operating DAR Services Mean That Computerized Planning and Scheduling Is Necessary for Systems of Realistic Size. This Research Studies an Electric Autonomous Fleet Size with Mix Dial-A-Ride Problem. The Goal of The Problem Is to Minimize a Weighted Objective Function Consisting of The Total Travelling Costs of All Vehicles, Users' Excess Ride Time Costs and Vehicles' Acquisition Costs While Satisfying Customer Service Level Constraints Along with Battery Level Management and Recharge Times Management Constraints. In This Variant of The Dial-A-Ride Problem, Recharging at Any of The Available Charging Stations Is Allowed. A Cluster-First, Route-Second Genetic Algorithm Is Proposed to Solve the Problem, Where the Clustering Is Performed by Choosing the Fleet Size and Assigning the Customers to The Fleet Using a Genetic Algorithm (GA), Then the Primary Routes Are Developed by A Routing Heuristic, Finally the Charging Stations Will Be Inserted to The Algorithm Using an Insertion Technique. The Performance of The Proposed Method Is Tested by Using Benchmark Instances of a Related Problem from The Recent Literature. The Proposed Method Has Achieved Solutions Comparable with The Current State-Of-Art Methods. The Computational Results Show That the Proposed Method Is Effective in Finding Comparable Solutions with The Current State-Of-Art Method. New Instances, Some of Which Include First-Mile Feeder Services, Are Generated Based on The Data from Uber Technologies Inc. Tests Performed on New Instances Demonstrate That the Minimum Possible Fleet Size Does Not Always Result in Minimum Costs. Moreover, The Tests Show That Integration of The Feeder Services into Dial-A-Ride Services Increases Ride-Sharing Ridership.
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Today, it is essential to achieve competitive market interests and since attention to environmental issues and reduced raw resources and energy has increased, design of a suitable supply chain network can be a great help in this regard. To this end, in this study, a multi-product closed-loop green supply chain has been designed. The innovative aspect of the research is the establishment of specific multiple objective functions in the field of transportation-based energy management, which is an optimization model for the green closed loop supply chain with four optimization objectives of carbon dioxide, transportation cost, energy, and waste, and the relevant model is designed by innovative genetic algorithms. And the refrigeration simulation is solved. In order to integrate the target functions, the weighting method of the targets has been used, the weight of the targets has been chosen according to the experts' opinion. The results of the algorithms' efficiency show that when we consider the four objective functions at the same time, the genetic algorithm performs better. Also, by comparing the results of these two algorithms, the efficiency of the genetic algorithm for the two objective functions of energy and cost was proven, and the efficiency of the refrigeration simulation algorithm was determined for the two objective functions of carbon dioxide and waste. This paper helps managers to benefit from green transportation and improving environmental performance and reducing costs in the entire supply chain as a complementary strategy in order to gain a sustainable competitive advantage.

In recent years, the use of smart numerical search techniques in active control of structure has been considered by researchers. Genetic algorithm is one of these methods that is based on natural evolution patterns. Here, the genetic algorithm is used to minimize the constrained objective function of the structure and determine the appropriate control forces. By connecting the sensor to the degrees of freedom of the structure, nodal displacement is monitored at each time step and genetic algorithm determines the proper control forces by using constrained objective function and solving dynamic equation of the structure. Choosing appropriate constrained objective function according to the structure conditions has a great impact on the efficiency of the proposed method. This function is obtained by combining the structural constraints and the assumed weighting factors for each constraint. Then, the weighting factors are calculated from the optimization methods. Such a process, leads to achieve a new active control method based on using genetic algorithm. The efficiency and accuracy of the proposed algorithm are investigate by controlling the oscillations of several structures. The results show that the proposed method is able to reduce structural oscillations effectively.