MATLAB Genetic Algorithm Toolbox Research Articles

Modelling and Optimization of Electrostatic Discharge Machining Parameters Using Genetic Algorithm

Electrostatic Discharge is a phenomenon that results from separating two dissimilar solid surfaces that were in contact. It results from the transfer of electrons from one surface to the other. Hence, one of the surfaces is positively charged, while the other surface becomes negatively charged. This phenomenon takes place during single point diamond turning of contact lenses polymers such as ONSI-56. Since higher electrostatic discharge adversely affects surface roughness, there is need to optimize electrostatic discharge machining parameters. The aim of this study is to develop an electrostatic discharge model and optimize the electrostatic discharge machining parameters during single point diamond turning of ONSI-56. Multiple regression has been utilized for model development and Genetic Algorithm (GA) has been used to optimize the model parameters. The GA toolbox in MATLAB is used for optimization in this study. In this study, cutting speed, depth of cut and feed rate are the model variables, while electrostatic discharge is the response variable. The regression model’s effectiveness has been evaluated by the R2 value method. The model has an R2 value of 88.29%, indicating that there is a strong collective significant effect among the control and response variables. Additionally, the results indicated that cutting speed and feed rate are the most significant predictors, while depth of cut is a slightly less significant predictor. The optimization process yields the following optimal values for cutting speed, feed rate, depth of cut and ESD, respectively: 200 rpm, 12 mm/min, 10 µm and 1,28 kV. An assessment of population size against objective function execution time has revealed that a population size of 500 has the shortest execution time of 14.23 seconds. The results have revealed that the optimization technique (GA) is efficient in ESD process optimization during single point diamond turning of ONSI-56.

Design and optimization of complex mechanism flip shaping subsystem based on genetic algorithm and rigid-flexible coupled dynamic model.

In this paper, the cam connecting rod system of the high-speed group vertical machine flipping shaping mechanism is the research object. In order to solve the key problem that the flipping shaping mechanism cannot accurately complete the action when the vibration of the mechanism is large. In this paper, the finite element method is used to construct the dynamic model of the connecting rod subsystem of the flipped shaping mechanism. And The dynamic model of cam roller subsystem is established by centralized parameter method. Based on the MATLAB Genetic Algorithm toolbox and using Newmark's method, the dynamic equations of the flipped plastic mechanism system are solved. The optimal parameters of the connecting rod of the mechanism, the cam profile curve and the swing power and swing torque of the mechanism at different speeds are analyzed. The results show that the speed and convex contour line are important factors affecting the performance of the mechanism. And the pendulum force (swing torque) is the main cause of the vibration of the mechanism on the frame. Therefore, the mechanism pendulum dynamic and the swing moment are selected as the objective functions of the optimization model. By selecting the node parameters of the sixth order spline motion law and the cross-section parameters of the connecting rod as the design variables. The cam linkage system is optimally designed to obtain the optimal value. Finally, the optimal design of the flipped shaping mechanism was analyzed and compared with the original mechanism.

Optimizing the Aerothermomechanical Behavior of Sandwich Panels Using Genetic Algorithm

A multi-objective optimization of a sandwich structure is presented to maximize the fundamental frequency and the critical buckling temperature while minimizing the cost of the laminate, alongside with keeping the weight lower than a preset value. The sandwich structure was chosen to be made of a high-stiffness, relatively expensive material (carbon/epoxy), and a low-stiffness, relatively low- cost material for the core (glass/epoxy). The governing equations are derived using the principle of virtual work, based on the first-order shear deformation plate theory. MATLAB Genetic Algorithm toolbox is used to find the optimum stacking sequence and ply thicknesses to achieve the optimal solution. The ground structure was used to set the initial ply stacking sequence and then the algorithm assumes the existence or absence of each ply and their thicknesses. The optimum buckling temperature and natural frequency values are introduced for different aerodynamic pressure values and under simply supported boundary condition.

Investigation of cutting temperature, cutting force and surface roughness using multi-objective optimization for turning of Ti-6Al-4 V (ELI)

Titanium alloys are getting prime importance in the areas like aerospace, biomedical, marine and many other applications where superior properties like higher strength to weight ratio, good fracture toughness and excellent corrosion resistance. Ti-6Al-4 V Because of the controlled interstitial element of iron and oxygen, Extra Low Interstitial (ELI) has exceptional qualities. During the turning of Ti-6Al-4 V, the impacts of four cutting parameters, namely cutting speed, feed, depth of cut, and tool nose radius, on responses such as surface roughness, cutting temperature, and cutting force, are explored (ELI). A total of 81 trials were carried out in a dry environment. Mathematical models for surface roughness, cutting temperature and cutting force are developed. Genetic algorithm toolbox of MATLAB is used for Multi objective optimization of cutting force, cutting temperature and surface roughness. The minimum cutting force of 12kgf, the least surface roughness as 0.307 µm and the lowest cutting temperature is 43.1 °C have been reported. The optimum values for cutting force, cutting temperature and surface roughness for multi-objective optimization have been presented by Pareto optimal solutions. The research work presented in this paper determines the values of process parameters of turning for achieving desirable combination of surface roughness, cutting force and cutting temperature on Ti-6Al-4 V(ELI)

Research on transformer fault diagnosis based on genetic algorithm optimized neural network

Aiming at the problem of transformer fault diagnosis in power system, a fault diagnosis method based on genetic algorithm optimization BP neural network is proposed. The BP neural network structure was established based on the traditional three-ratio fault diagnosis method. The concentration of dissolved gas in transformer oil was taken as the input value of the neural network, and the fault working state of transformer was taken as the output value. The established neural network is used to diagnose transformer faults. In order to avoid BP neural network falling into the problem of local optimal value, the weights and thresholds of the neural network are optimized by genetic algorithm. By using the genetic algorithm toolbox in Matlab to establish the genetic algorithm network structure, the transformer fault data as input for the performance of the neural network test. The results show that the neural network optimized by genetic algorithm has a high classification effect on transformer fault types, and the neural network optimized by genetic algorithm has a higher diagnostic efficiency and accuracy than BP neural network.

Optimum Design of Reinforced Concrete One-Way Ribbed Slabs Using Genetic Algorithm

This paper deals with the problem of optimum design of reinforced concrete one-way ribbed using genetic algorithms. ACI-Coefficient method is used for the structural analysis and design of slabs. The cost function represents the cost of concrete, steel, and formwork for the slab. The design variables are taken as slab thickness, spacing between ribs, lower width of ribbed, upper width of ribbed, depth of rib, depth of beam, shear reinforcement spacing of ribs, bar diameter and spacing of top slab, depth of the neutral axis from bottom fiber, beam width, and areas of flexural reinforcement at moment critical section along ribs and beams. The constraints include the constraints of the joist construction constraints stated by ACI-code, constraints on the top slab thickness to satisfy fire resistance, constraints on the areas of steel reinforcement to satisfy the flexural and the minimum area requirements, the constraints on the total slab thickness to satisfy deflection, and flexural behavior. The parameters were taken in this study included: the span length(4-12)m, the compressive of concrete(25-60) MPa, the strength of steel (345-600)MPa, the live load (2-7)KN/m2, unit cost ratio(cost of concrete/cost of steel)(0.1-0.35), aspect ratio (1-2), the formwork cost(10,000-22,000)I.D. The results show that, when discussed the span length, the ratio of (total slab depth/span length) should be (1/18-1/4) using G.A, to get the optimum slab design. It is also concluded that the optimum ratio of (depth of rib/lower rib width) is to be ranged within (1.243-2.917) using G.A. The optimum (rib spacing/span length) ratio is found to be ranged from (1/40- 1/5) using G.A, the results also showed that the range of the optimum (slab thickness/spacing of rib) ratio is found to be ranged as (1/4-1/2) using G.A, in order to find the optimum solution.A computer program is written using MATLAB for the implementation of structural analysis and the design of one-way ribbed slabs by the ACI coefficient method. The optimization process is performed using the built-in genetic algorithm toolbox of MATLAB.

Comfort optimization of a new type of foot mechanism for lower extremity exoskeleton

In order to reduce the impact caused by the contact between the foot and the ground when wearing the lower extremity exoskeleton under the condition of high load, this paper proposed an exoskeleton foot mechanism for improving the foot comfort, and optimized the key index of its influence on the comfort. Firstly, the physical model of foot mechanism was established based on the characteristics of foot stress in gait period, and then the mathematical model of vibration was abstracted. The correctness of the model was verified by the finite element analysis software ANSYS. Then, this paper analyzed the influence of vibration parameters on absolute transmissibility based on vibration mathematical model, and optimized vibration parameters with MATLAB genetic algorithm toolbox. Finally, this paper took white noise to simulate the road elevation as the vibration input, and used the visual simulation tool Simulink in MATLAB and the vibration equation to construct the acceleration simulation model, and then calculated the vibration weighted root mean square acceleration value of the foot. The results of this study show that this foot comfort mechanism can meet the comfort indexes of vibration absorption and plantar pressure, and this paper provides a relatively complete method for the design of exoskeleton foot mechanism, which has reference significance for the design of other exoskeleton foot and ankle joint rehabilitation mechanism.

Impact of Dynamic Storage Management of Wetlands and Shallow Ponds on Watershed-scale Flood Control

This paper presents a case study on the impact of dynamic management of wetland storage on downstream flood control at the Cypress Creek Watershed in Houston, Texas. Dynamic storage management is performed by optimizing the schedule of flow releases from managed wetlands for minimizing downstream inundation. The novelty of this study is to increase the available storage capacity of wetlands on a watershed scale and thus improve their performance on flood mitigation. The main objective of this study is to compare the extent of downstream flood inundation with and without dynamic management of wetland storage. This study used HEC-HMS for the overland flows and the level pool routing in the wetlands and HEC-RAS for simulating river inundation. Wetlands were implemented in the midstream region of the watershed. The schedule of optimal wetland flow releases were determined using the MATLAB Genetic Algorithm toolbox. The optimization results indicate that, when wetland size exceeds a fairly low threshold, dynamic storage management of wetlands can significantly reduce flood area, flood depth and flood duration at the downstream region of the watershed compared with the case without dynamic storage management.

Modelling and Analysis of Deregulated Electricity Market Operations

Deregulation in power industry is restructuring of the rules and economic incentives that government set up to control and drive the electrical power industry. Restructuring in electricity industry will create new business opportunities where new firms selling new products and services will appear, consumers will have alternatives in buying electricity services, and new technologies such as metering and telecommunication devices will develop. Bidding by participants is one of the major challenges. To overcome this issue, strategic bidding models are being developed based on market structure, auction rules and bidding protocols. This paper proposes to formulate an optimal bidding strategy algorithm for DisCos and analyze its impact in the market operations. The bidding optimization is aimed at developing bids that get selected in the market operations and simultaneously maximizing the total profit of DisCos and minimizing power imbalances. Power Exchange market, which is simple yet enables competition, will be considered. This is proposed to be done using MATLAB coding and Genetic Algorithm tool box. Multiple simple bids are modeled. Power imbalance for each of the possible cases is computed. Decision variables (L1,L2,L3) and (C1,C2,C3) are used in order to compute objective functions i.e. Minimization of Power imbalances and maximization of profit, using MATLAB coding and GA tool box.

Modeling and optimization of process fired heaters

In this study, a mathematical model associated with genetic algorithms (GAs) is developed to optimize the design of process fired heaters where the objective function is the total annual cost. The mathematical model considering operational and geometric constraints is used to design all subsections of a furnace including the radiant chamber, the convection and stack sections. The proposed model allows the economic optimization using the MATLAB genetic algorithm toolbox. This procedure is implemented to design and optimize fired heaters. Two case studies are considered. First, by comparing the results with literature, it is shown that the developed model can be successfully used in design with acceptable accuracy. In the first case study, based on TAC (total annual cost) minimization, two optimization scenarios with two sets of decision variables using GA are applied to determine the optimal economic design. It is demonstrated that by considering the tube diameter as a decision variable (scenario B), the TAC minimization approach does not present a realistic optimal design, because the crude oil pressure drop is out of a permissible range. Finally, by including the pumping cost into the operating cost and modifying the objective function, another optimization approach based on MTAC minimization is applied for the same optimization scenarios. In this case, scenario B results in the economic/realistic optimal design by up to 2.48% cheaper than Original Design.

Wind Micro-Turbine Networks for Urban Areas: Optimal Design and Power Scalability of Permanent Magnet Generators

This work is focused on the design optimization of electrical machines that are used in small-scale direct-drive aerogenerators. A ducted wind turbine, equipped with a diffuser, is considered due to its enhanced power capability with respect to bare turbines. An annular type Permanent Magnet brushless generator is integrated in the turbine structure: the stator coils are placed in the internal part of the diffuser, whereas the permanent magnets are on an external ring connected to the turbine blade tips. Moreover, as regards the stator windings, the Printed Circuit Board (PCB) technology is investigated in order to exploit its advantages with respect to conventional wire coils, such as the increased current density capacity, the reduction of costs, and the enhanced precision and repeatability of the PCBs. An original design procedure is presented together with some scalability rules. An automated tool has been developed in order to aid the electrical machine designer in the first design stages: the tool performs multi-objective optimizations (using the Matlab Genetic Algorithm Toolbox), coupled to fast Finite Element analysis (through the open-source software FEMM) for the evaluation of the electromagnetic torque and field distribution. The proposed procedure is applied to the design of an annular PM generator directly coupled to a small-scale turbine for an urban application.

Unified Kinetics Modeling of Isothermal Bainite Transformation in 60Si2CrA Steel

In this study, isothermal bainite transformation kinetics in hypo-eutectoid steel 60Si2CrA was investigated. Isothermal transformation at temperature ranging from [528 K (255 °C)] to [653 K (380 °C)] after austenitization was performed on a quenching dilatometer. Based on the dilation data, a shear bainite kinetics model was established using the internal state variable (ISV) approach. The model is coupling with bainite phase nucleation and growth, and the incubation time is characterized by the proposed normalized bainite nucleus radius and normalized radius growth rate. Material constants within the unified kinetics model were calibrated with genetic algorithm (GA)-based optimization methodology using GA toolbox in MATLAB. Furthermore, fairly close agreement between model predicted and tested data was achieved, and predicted evolution of ISVs in the model was illustrated. The proposed shear-type bainite kinetics model may be applied to other hypo-eutectoid steels.

Analysis method of dynamics and structure optimization for sealing mechanism under reducing vibration condition

The paper highlights the importance of optimal design model in the design evaluation of complex sealing mechanism. Sealing mechanism of the high-speed assembly machine is taken as the research object and construct rigid-flexible coupling dynamic equation. Dynamic characteristics of the sealing mechanism are analyzed by MATLAB. The maximum dynamic stress and reducing the acceleration of the end F are regarded as the optimization object. Cam profile curve parameter and the link cross section parameter are designed as the variables to construct the optimization design model of the whole sealing mechanism, and nonlinear constraint problem is transformed into a linear constraint problem. Based on the MATLAB genetic algorithm toolbox, the optimal design model of the sealing mechanism is solved, and the optimal result is obtained.

Algoritmos genéticos y PSO aplicados a un problema de generación distribuida.

En este informe se presentan los resultados de la aplicación de algoritmos genéticos y PSO (Particle Swarm Optimization), para optimizar un problema de generación distribuida (GD) de potencia que debe cumplir ciertas restricciones. Para la implementación del algoritmo genético se utiliza el toolbox de Matlab ya implementado variando algunos parámetros como fracción de mutación, población etc. Lo anterior para compararlo con la función fmincon ya implementada dentro del ambiente Matlab y sacar conclusiones en cuanto a tasa de convergencia y error entre los datos. El Algoritmo PSO fue implementado teniendo en cuenta procesos estocásticos basados en suerte, definiendo propiedades intrínsecas a él, tal como tamaño de población, factor de inercia etc.

Optimization Design of Magnetic Gear Based on Genetic Algorithm Toolbox of Matlab

Concentric magnetic gear (CMG) is a transmission gear with a novel structure and a promising prospect. However, the calculation of its electromagnetic field is more complicated. In this paper, an exact analytical method is used to calculate the magnetic field distribution and electromagnetic torque. The genetic algorithm optimization toolbox of matlab (GAOT) provides a good tool for its optimization design. The optimization results show that the genetic algorithm is practicable and effective, and provide a new approach for the analysis and design of the concentric magnetic gear. It is significant for the research and application of the concentric magnetic gear.

Optimization design and economic analyses of heat recovery exchangers on rotary kilns

Heat loss from the kiln shell accounts for a certain proportion of total energy input in a rotary kiln. To reduce the heat loss, a novel heat recovery exchanger installed on the kiln shell is proposed to preheat water in this paper. Numerical simulations and experimental measurements are carried out to investigate the flow and heat transfer characteristics of the heat recovery exchanger. Then, multi-objective optimization models are proposed with the heat transfer area, the pressure drop and the modified entropy generation number set as objective functions. The mathematic models describe the relation between heat transfer rates and the design parameters of the heat recovery exchanger, i.e. the tube length, the tube numbers and the tube diameter. With the aid of the Matlab genetic algorithm toolbox, the optimized design parameters are obtained. Finally, economic analyses of a practical heat recovery system with nine heat recovery exchangers are proposed to estimate the saving of the fuel consumption. The optimization results show that the Pareto solutions are the trade-off between the two conflicting objective functions and give some final decisions considering specific requirements for the designers. Besides, the optimized results of heat transfer areas and pressure drop of the heat recovery exchanger are decreased by at least 18% and 11%, respectively. As the modified entropy generation number due to heat transfer and fluid friction decrease, the corresponding heat transfer area and pressure drop decrease, respectively. The economic analyses indicate that the heat recovery system is profitable and the revenue comes from the fuel savings.

A genetic algorithm-based integrated design environment for the preliminary design and optimization of aeronautical piston engine components

The enhancement of the early design stages, in the production of aeronautical engines, has been shown decisive, for developing efficient and reliable final products. Nevertheless, in most of industrial engineering design problems, the amount of design variables is large. Moreover, several nonlinearities characterize the behaviour of the physical phenomena involved and the derivatives are seldom known for all the functions. Besides, objective functions exhibit several local extremes, whereas the designer as well as the practitioner is usually interested in the global one. In this context, stochastic and evolutionary optimization have been shown capable to provide reliable solutions while keeping the computational cost at a reasonable level. Existing tools for the design and optimization of engine components deal with the optimal and detailed design of specific engine components, thus requiring several computational time and efforts to gain optimized design parameters. Hence, existing tools fit for later design phases. Conversely, this paper proposes an integrated design and optimization environment, for automatically designing optimal aeronautical piston engine configuration, still in the conceptual design stage. The optimization is performed using the MATLAB genetic algorithm (GA) toolbox®, while the automatic design of the optimized components is carried out in cascade to the optimization phase. In particular, a single-objective GA is used to evaluate the optimal dimensions of engine components related to motion, namely: crankshaft, connecting rods and screws, flywheel, propeller shaft and torsional vibration damper. For testing the efficiency of the integrated environment, the conceptual design of components of a 4-in-line Diesel aeronautical piston engine is proposed, starting from an existing similar engine. Results show a reduction of the 20 % of weight of the crankshaft in comparison to the original configuration. The proposed environment seems to be a promising tool for a fast and reliable conceptual design of piston engines for aeronautical purposes.

Buckling optimisation of sandwich cylindrical panels

AbstractIn this paper, the buckling load optimisation is performed on sandwich cylindrical panels. A finite element program is developed in MATLAB to solve the governing differential equations of the global buckling of the structure. In order to find the optimal solution, the genetic algorithm Toolbox in MATLAB is implemented. Verifications are made for both the buckling finite element code and also the results from the genetic algorithm by comparisons to the results available in literature. Sandwich cylindrical panels are optimised for the buckling strength with isotropic or orthotropic cores with different boundary conditions. Results are presented in terms of stacking sequence of fibers in the face sheets and core to face sheet thickness ratio.

Study on the Optimization of the Blending Ratio of the Magnetic Fiber Blended Yarns by Using MATLAB Genetic Algorithm Toolbox

Use the magnetic fiber, bamboo fiber and cotton fiber to spun into yarn. Analysis the properties of the blended yarn under different conditions of blended ratio. Use MATLAB genetic algorithm toolbox to ensure that the yarn blending ratio is optimal. It shows that the optimal blending ratio of three kinds of fiber of magnetic, bamboo and cotton is 67%, 13%, 10%.

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.