Genetic Algorithm Toolbox Research Articles

Energy Efficient Coverage Optimization in Wireless Sensor Networks based on Genetic Algorithm

There has been a great deal of attention paid to Genetic Algorithm (GA). The algorithm, as a methodology, is a multi objective methodology which can be used in different fields such as self-organizing wireless sensor network. The technique examines the applied parameters and at the same time takes into consideration the fitness function by the way of or considering the whole operational modes in produced feasible states. Majority of the GA implementations in clustering algorithm only deal with optimization of few parameters including coverage and energy consumption with noticeable effect on network quality. Keeping network coverage can be modeled as mathematical programming problem which is featured with heavy load of computation. On the other hand, wireless sensor networks (WSNs) can be of dynamic nature, if so it needs to have proper reaction to events; so that slightest management decision may lead to considerable problems on the quality of the network. This problem is dealt with in this study through a hybrid method in MATLAB with the help of Genetic Algorithm toolbox and custom codes. The optimum solution was obtained by mathematical algorithm that conforms to all the mentioned parameters.

Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration.

This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network parameters must stabilize the system in each iteration in addition to approximating the critic and the cost. It is found that the critic updates according to the Hamilton-Jacobi-Bellman formulation sometimes lead to the instability of the closed-loop systems. In the proposed work, a novel critic network parameter update scheme is adopted, which not only approximates the critic at current iteration but also provides feasible solutions that keep the policy stable in the next step of training by combining a Lyapunov-based linear matrix inequalities approach with PI. The critic modeling technique presented here is the first of its kind to address this issue. Though multiple literature exists discussing the convergence of PI, however, to the best of our knowledge, there exists no literature, which focuses on the effect of critic network parameters on the convergence. Computational complexity in the proposed algorithm is reduced to the order of (Fz)(n-1) , where n is the fuzzy state dimensionality and Fz is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experiment on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system.

Multi-Objective Optimization Model and Solution for Easily Broken Material Loading Based on Genetic Algorithms Under VMI

The material loading operation plan by artificial preparation has been unable to meet the requirements of the timely delivery when the third party logistics enterprises are involved in vendor managed inventory strategy. According to the characteristics of the easily broken material the multi-objective optimization model was established. The objective functions of this model were the vehicle loading weight utilization rate and the volume utilization rate. The five con- straints were introduced. The optimal solution of the model was obtained by genetic algorithm. The solving process was introduced including the pre processing coding, the individual coding, the fitness selection and the genetic operation. The genetic algorithm program was designed by using MATLAB genetic algorithm toolbox. The easily broken material boxes loading optimization of the VMI distribution center was as an example to verify the validity of the program. Thus a whole solution which contains the optimization model and the solution procedure was provided for the easily broken material loading.

Force Analyzing and Parameter Optimization of the Telescopic In-Pipe Robot

Typical wheeled or tracked robot could hardly apply to middle or small diameter underground pipelines laid by trenchless technology. Aiming at this kind of pipeline’s characteristics, this paper puts forward a basic structure of telescopic in-pipe robot. To provide necessary theoretical basis for the device selection, the mechanical model and force analyzing are given in detail. The speed of robot, as well as the motor torque of locking mechanism, could be expressed as the function of the robot’s structural parameters. As a result, the robot’s structural parameters take influence on its performance. In order to achieve the best performance, it is necessary to use the multi-objective optimization method to select these parameters. Using the genetic algorithm toolbox, the optimal solution of these parameters was obtained. Based on this, the motor torque of locking mechanism is minimum while the speed of the robot is maximum.

Developing a new model for dynamic vendor managed inventory with considering time value of money

This paper investigates a new dynamic two-echelon single-vendor multiple-buyer supply chain model under vendor managed inventory assumptions, which named dynamic vendor managed inventory, named DVMI, model. The DVMI model is formulated in order to find out the optimal sales quantity for each buyer at each period by maximising the profit of supply chain. All parameters are considered deterministic and delays in the payments are permissible. In addition, the relationship between sales quantity and sales price is assumed to be linearly. Implementation of proposed DVMI model is analysed with a case study that carried out at an Iranian dairy products company. The DVMI model is solved by using both LINGO and MATLAB genetic algorithm and direct search toolbox. Furthermore, sensitivity of model respect to the number of selected buyers in each period parameter is analysed.

English

In power distribution networks, nonlinear loads produce harmonic currents that can pass through other sensitive locations in the power system and eventually back to the source. Harmonic currents can produce a variety of effects that are harmful to a power system structure. Consequentially, the deployment of new cost and energy efficient passive, active and hybrid filtering techniques is important for the utilities and consumers alike. This paper presents an application of the Genetic Algorithm toolbox (GA) provided by MatLab software in order to find the optimal sizing of parameters of C-type passive filters based on minimization of the total investment cost of the proposed filters. Load power factor (PF), load voltage total harmonic distortion (THDV) and supply current total demand distortion (TDD) are considered as the main constraints in this paper. Besides, capacitor loading duties are taken into account in compliance with IEEE Standard 18-2002 guidelines. Various numerical results are presented to validate the proposed approach.

Joint Optimization Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the genetic algorithm toolbox. The parametric model of the planar truss is established by the ANSYS Parametric Design Language. Solutions are obtained using the first-order method native. Genetic algorithms don’t always display better properties than others. Finally, a joint optimization method is proposed, which combines MATLAB genetic algorithm toolbox and the numerical algorithm based on the quasi-Newton method. The method is identified through the numerical example of the two-bar truss. The results indicate the joint optimization method can always converge to the global optimal solution.

Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide

Cemented carbide has been investigated as a useful material for the fabrication of micro devices. Focused ion beam (FIB) micro-milling has been found to be one of the most appropriate methods for the fabrication of micro devices. The experimental FIB micro-milling on cemented carbide have been conducted according to the L16 orthogonal array of Taguchi technique. Beam current, extraction voltage, angle of beam incidence, dwell time and percentage overlap between beam diameters have been considered as process variables of FIB micro-milling in experimental design. Material removal rate (MRR) and surface roughness have been determined experimentally for FIB micro-milling of cemented carbide and beam current has been identified as the most significant parameter. The minimum surface roughness of 5.6nm has been reported on cemented carbide, which is not a usual practice to achieve on such polycrystalline material, and hence it may be considered as a significant research contribution. Maximum MRR of 0.4836μm3/s has been reported. Moreover, genetic algorithm toolbox of MATLAB has been utilized for multi-objective optimization between MRR and surface roughness. The corresponding optimum values of MRR and surface roughness for multi-objective optimization have been represented by pareto optimum solution generated by genetic algorithm. The research work presented in this paper determines the setting of process parameters of FIB micro-milling for achieving a specific combination of MRR and surface roughness on cemented carbide.

Study on Energy Consumption Prediction of Liquor-Making Based on GA-BP Neural Net

Aimed at the Liquor-making with the characteristic of huge consumption of raw materials and energy,this paper presents a predictive method of Liquor-making energy consumption based on GA-BP Neural Net.Genetic algorithm of mixed coding was used to optimize the structure and initial values,and then BP algorithm adjusted weight and thresholding accurately.Construct the B-P model by using the neural networks toolbox of MATLAB. Optimize calculation based on MATLAB genetic algorithm toolbox using the real data of water consumption,electricity consumptionand steam consumption.The prediction results agrees well with the real data.Finally factual data are used to validate the validity and the rationality of the designed result .Liquor-making energy consumption mainly is water consumption,electricity consumption and steam consumption.Results in some extent can reflect the liquor enterprise energy consumption.Experiment shows that the method is effective for the liquor enterprise energy consumption.prediction.

OPTIMISING STEEL PRODUCTION SCHEDULES VIA A HIERARCHICAL GENETIC ALGORITHM

This paper presents an effective scheduling in a steel-making continuous casting (SCC) plant. The main contribution of this paper is the formulation of a new optimisation model that more closely represents real-world situations, and a hierarchical genetic algorithm (HGA) tailored particularly for searching for an optimal SCC schedule. The optimisation model is developed by integrating two main planning phases of traditional scheduling: (1) planning cast sequence, and (2) scheduling of steel-making and timing of all jobs. A novel procedure is given for genetic algorithm (GA) chromosome coding that maps Gantt chart and hierarchical chromosomes. The performance of the proposed methodology is illustrated and compared with a two-phase traditional scheduling and a standard GA toolbox. Both qualitative and quantitative performance measures are investigated.

Research on Design of Free Combat Attack and Defense Action Based on Wireless Sensor Motion Recognition

This paper introduces genetic algorithm in sensor path loss model, improves the target recognition function of sensor, and obtains the sensor target recognition method with relatively high resolution. We use MATLAB software, M function and genetic algorithm toolbox of MATLAB to design the algorithm, and apply sensor target recognition method in the identification of free combat action, and design the free combat movement recognition system based on wireless sensor. In order to verify the effectiveness and reliability of the system, this paper recognizes a set of continuous and rapid transformation of free combat movement, and obtains the curve of action recognition number changing with time, and the continuous action is displayed in the form of visualization. It provides the technical reference for the design of the free combat movement.

Research on Cultural Industry Distributed Network Control Model Based on Genetic Algorithm

The network economy era brings new challenges to all aspects of society, and the construction of network culture industry also brings an urgent request. Due to the complexity of network culture industry, the construction and study of cultural network industry has certain challenging. In order to realize the massive network industrial data analysis, we introduce the genetic algorithm into the network control model, and put forward the distributed network control model based on genetic algorithm, which can realize the massive data analysis. In order to verify the effectiveness of the algorithm, this paper uses MATLAB genetic algorithms toolbox to debug the algorithm, and obtains the genetic algorithm fitness curve and the data transmission quantity statistics. It provides the theory reference for the construction of network culture industry.

A Simple GA Based Approach for Multi-Objective Optimization of Machining Parameters

A simple approach to multi-objective optimization of machining parameters is presented. Regression analysis of experimental data is carried out to obtain the correlation between cutting parameters and response variables. Finally, Genetic Algorithm (GA) toolbox of MATLAB is used to carry out multi-objective optimization of two objective functions (surface roughness “Ra” & material removal rate “MRR”). Genetic algorithm is found to be a powerful tool for multi-objective optimization of machining parameters in this study.

Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system

Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process.

Optimized Allocation of DGs to Improve System Reliability Based on Loading Effects

The results obtained by recent researches on distributed generations (DGs) have shown that distribution systems adequacy can be improved using DGs. The features supported by DGs improving the system reliability are different. DGs can help decrease devices failure rate by improving the current passing through each distribution line and any device of system. In this point of view, DGs have the same effects as redundant lines. It is interesting that by minimum capacity of DG sources, the maximized reliability improvement can be achieved. The placement of DGs is one of the most important parameters of their planning. In this paper, a novel method is proposed to optimize the placement of DGs based on decreasing failure due to the enhancement of loading effects. Average system interruption frequency index is selected to define the objective function. Moreover, the optimization problem is solved by genetic algorithm (GA). MATLAB GA toolbox is used to implement the optimization problem solving. The method is applied to a 20 kV, 33-bus realistic system of Tehran Regional Electrical Company. Test results illustrate by optimal allocation of 2,000 and 1,000 kVA DGs generation capacity according to proposed method, 12.32 % and 6.35 % ASIFI decrement can be achieved, respectively. Furthermore, the system in view point of protective device placements, maximum DGs generation capacity, the critical lines and the most important sections are studied.

Matlab�s GA and Optimization Toolbox: A Fourbar Mechanism Application

This study presents an optimization approach for synthesis of planar mechanisms. A four bar mechanism is chosen for an application example. This mechanism is studied with the constraints assigned. Genetic Algorithm (GA) is applied during optimization study. GA in Optimization Toolbox is then compared with nonlinear constrained numerical optimization command; fmincon in Matl ab©. Different case studies are performed by considering different target points. These mechanisms are drawn using Excel© spread sheet to see their animations. An optimization example is presented here. Performances of both algorithms are then compared in terms of coupler curves precision points. Their use in designing a four bar mechanism is explored for its further use.

Simulation of clinker grinding circuits of cement plant based on process models calibrated using GA search method

Particle size distributions of obtained samples from several sampling campaigns were determined and raw data were mass balanced before being used in simulation studies. After determination of breakage function, selection function, Bond work index, residence time distribution parameters, and Whiten’s model parameters for air separators and diaphragms between the two compartments of tube ball mills, performance of the circuits was simulated for given throughputs and feed particle size distribution. Whiten’s model parameters were determined by GA (genetic algorithm) toolbox of MATLAB software. Based on implemented models for modeling and simulation, optimization of circuits was carried out. It increased nearly 10.5% and 15.8% in fresh feed capacity input to each tube ball mill. In addition, circulating load ratios of circuits are modified to 118% and 127% from low level of 57% and 22%, respectively, and also cut points of air separators are adjusted at 30 and 40 μm from high range of 53 and 97 μm, respectively. All applications helped in well-operation and energy consumption reduction of equipments.

Rail Vehicle Vibrations Control Using Parameters Adaptive PID Controller

In this study, vertical rail vehicle vibrations are controlled by the use of conventional PID and parameters which are adaptive to PID controllers. A parameters adaptive PID controller is designed to improve the passenger comfort by intuitional usage of this method that renews the parameters online and sensitively under variable track inputs. Sinusoidal vertical rail misalignment and measured real rail irregularity are considered as two different disruptive effects of the system. Active vibration control is applied to the system through the secondary suspension. The active suspension application of rail vehicle is examined by using 5-DOF quarter-rail vehicle model by using Manchester benchmark dynamic parameters. The new parameters of adaptive controller are optimized by means of genetic algorithm toolbox of MATLAB. Simulations are performed at maximum urban transportation speed (90 km/h) of the rail vehicle with ±5% load changes of rail vehicle body to test the robustness of controllers. As a result, superior performance of parameters of adaptive controller is determined in time and frequency domain.

Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox

In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.

Development of genetic algorithm toolbox using MATLAB in cutting tool path optimization

In this study, optimization of tool path is presented through simulation material removal using Finite Element Analysis (FEA) and control via Genetic Algorithm (GA). The computer code for GA is developed and is specifically designed using MATLAB programming. The developed GA toolbox was written in m-files script together with several built-in functions in order to import the text file generated by FEA software. This paper presents the solution for cutting tool path optimization which is the interaction toolbox that consists of the integration between MATLAB and ABAQUS. This developed toolbox facilitates the optimization process to be performed successfully on the tool path machining process. The optimization technique inherently assists in producing finishing product, with minimum chatter and static deflection problem. Key words: Genetic algorithm (GA), MATLAB programming, intelligent cutting tool path.