Genetic Algorithm Toolbox Research Articles

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Multi-axle vehicles are widely used in several applications such as transportation, industrial, and military field, because of its higher reliability in comparison with conventional two axles vehicles. Despite that, there is a paucity of research studies that consider lateral stability enhancement of these vehicles, especially on rough terrain. This simulation-based research study fills this gap and introduces a new adaptive Active Rear Steering (ARS) controller that improves the lateral stability of an 8x8 combat vehicle for rough-terrain operation. The developed controller is designed utilizing the Integral Sliding Mode Control theory (ISMC) based on Gain-Scheduled Linear Quadratic Regulator (GSLQR). Besides, the GSLQR control gains are optimized by a Genetic Algorithm (GA) toolbox using a new synthesized cost function to ensure asymptotic stability. Furthermore, a new Adaptive-ISMC (AISMC) is introduced by using genetic programming to generate control equations that can replace the developed high-dimension GSLQR gains and facilitate future hardware implementation. The developed controller is evaluated by performing a series of simulation-based Double Lane Change (DLC) maneuvers on several rough terrains. The evaluation is conducted for both high friction and slippery surfaces at high and moderate speed, consequently. The results show high fidelity and robustness of the developed controller in comparison with a previously designed optimal LQR controller.

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.

On the suitable superstructure thermoeconomic optimization of a waste heat recovery system for a Brazilian diesel engine power plant

Brazilian thermal power plants were designed to operate as peaking plants. The diesel engine power plants in Brazil eliminate copious amounts of residual heat through cooling water and exhaust gases. The current scenario has led to an increase in the operational times of the plants. An attractive solution for increasing power and efficiency in this situation is waste heat recovery (WHR). A variety of technologies and configurations can be used for WHR. The selection of the best WHR system is not a simple task, and the solution should not be generalized for different applications. In this work, a superstructure method is used for structural and parametric thermoeconomic optimization, selecting the best WHR for a Brazilian diesel engine power plant operating scenario. Three technologies are considered: the conventional Rankine cycle (CRC), the organic Rankine cycle (ORC), and the Kalina cycle (KC). The thermoeconomic models were developed using Engineering Equation Solver software, and the optimizations were carried out using the genetic algorithm toolbox. Due to the specificity of the Brazilian scenario, the maximization of the gross profit rate function was the selected objective, allowing for the evaluation of 16 combinations of annual dispatch hours and variable unit cost of electricity. The results showed that, although different optimal structures and parameters can be obtained through optimizations, depending on the combinations of annual dispatch and unit cost of electricity, ORC has the highest gross profit rate and is always the best alternative. For an optimistic scenario of dispatch and variable unit cost of electricity, ORC produces up to 14.3 MW of net power, increasing the installed capacity of the 20 generator sets up to 8.2%.

A field development strategy for the joint optimization of flow allocations, well placements and well trajectories

One of the major goals that field planning engineers and decision makers have to achieve in terms of reservoir management and hydrocarbon recovery optimization is the maximization of return on financial investments. This task yet very challenging due to high number of decision variables and some uncertainties, pushes the engineers and technical advisors to seek for robust optimization methods in order to optimally place wells in the most profitable locations with a focus on increasing the net-present value over a project life-cycle. The quest to deliver a good quality advice is also dependent on how some uncertainties – geologic, economic and flow patterns – have been handled and formulated all along the optimization process. With the enhancement of computer power and the advent of remarkable optimization techniques, the oil and gas industry has at hand a wide range of tools to get an overview on value maximization from petroleum assets. Amongst these tools, genetic algorithms which belong to stochastic optimization methods have become well known in the industry as one the best alternatives to apply when trying to solve well placement and production allocation problems, though computationally demanding. The aim of this work is to present a novel approach in the area of hydrocarbon production optimization where control settings and well placement are to be determined based on a single objective function, in addition to the optimization of wells’ trajectories. Starting from a reservoir dynamic model of a synthetic offshore oil field assisted by water injection, the work consisted in building a data-driven model that was generated using artificial neural networks. Then, we used Matlab’s genetic algorithm toolbox to perform all the needed optimizations; from which, we were able to establish a drilling schedule for the set of wells to be realized, and we made it possible to simultaneously get the well location and configuration (vertical or horizontal), well type (producer or injector), well length, well orientation – in the horizontal plane –, as well as well controls (flow rates) and near wellbore pressure with respect to a set of linear and nonlinear-constraints. These constraints were formulated so as to reproduce real field development considerations, and with the aid of a genetic algorithm procedure written upon Matlab, we were able to satisfy those constraints such as, maximum production and injection rates, optimal wellbore pressures, maximum allowable liquid processing capacity, optimal well locations, wells’ drilling and completion maximum duration, in addition to other considerations. We have investigated some scenarios with the intention of proving the benefits of development strategy that we have chosen to study. It was found the chosen scenario could improve NPV by 3 folds in comparison to a base case scenario. The positioning of the wells was successful as all producers were placed in zones having initial water saturation less than 0.4., and all injectors were placed high water saturation zones. Moreover, we established a procedure regarding well trajectory design and optimization by taking into account, minimum dogleg severity and maximum duration for a well to be drilled and completed with respect to a time threshold. The findings as well as the workflow that will be presented hereafter could be considered as a guideline for subsequent tasks pertaining to the process of decision making, especially when it has to do with the development of green oil and gas fields and will certainly help in the placement of wells in less risky and cost-effective locations.

Optimal design of passive‐adaptive pendulum tuned mass damper for the global vibration control of offshore wind turbines

AbstractThis paper presents an optimal design procedure for a pendulum tuned mass damper (PTMD) to mitigate the global structural vibrations of offshore wind turbines (OWTs) in the fore–aft and side–side directions. The procedure is tested to the design of a PTMD to be applied to the 5‐MW benchmark baseline monopile wind turbine proposed by the National Renewable Energy Lab (NREL). The computation of wind and wave spectra, as well as the evaluation of the hydrodynamic and aerodynamic loads, is conducted by using an in‐house built MATLAB® routine working together with an ANSYS® 3‐D finite element (FE) global model for evaluating the resultant peak displacement response at the OWT hub by a power spectral density (PSD) analysis. In order to validate the OWT FEM model, a result comparison is made with the NREL OpenFAST, finding good matches between the two codes. An in‐house built genetic algorithm (GA) toolbox, coded in MATLAB®, is then used to optimally design the parameters of a PTMD with a simplified 2‐degrees‐of‐freedom (2DOF) model. The chosen GA fitness function targets the minimization of the peak response of the primary structure as evaluated by the 2DOF model. The design parameters of the PTMD are the flexural rigidity and damping, the mass ratio and pendulum length. After the 3‐D FE model of the OWT without any control device has been validated, and the PTMD has been optimized by the simplified 2DOF model, the performances of the PTMD are examined on a 3‐D global FE OWT + PTMD model in ANSYS®.

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.

Box-Behnken Design Optimization of Sand Casting Process Parameters

Sand casting is reputed for the manufacture of engine components as a result of its ease of operation. An assemblage of process parameters at optimal conditions leads to enhanced mechanical properties of automobile engine components. The Response Surface Methodology Design of experiment created an experimental layout for the sand casting process parameters and the various levels as applied in the production of engine pistons at the foundry. The Box-Behnken Design provided a matrix of 27 experiments to be conducted. Multiple linear Regression technique was employed to develop a mathematical model for the hardness of the aluminium alloy. The developed model was inputted into the evolutionary Genetic Algorithm tool box as an objective model. The optimal levels determined from the Genetic algorithm were used to carry out actual experiment in the foundry and the result was similar to the predicted hardness value of the developed model. Statistical ANOVA test conducted showed that the mathematical model was adequate with a R2 value of 81.02% and R2 (adjusted) value of 60.07%. The developed model has a p-value of 0.016 which indicates that the model was significant. The optimal values obtained for pouring temperature, vibration frequency, vibration time and runner size are 700oC, 31.52Hz, 59.998sec and 469.69mm2 respectively.

Error modeling and accuracy optimization of rotating ultrasonic vibration assisted EDM machine tool

Rotating ultrasonic vibration assisted EDM is a composite machining method that uses EDM to etch a metal surface and adds rotating and ultrasonic vibration. It has high efficiency and a wide range of workpiece materials. Aiming at the structural characteristics of rotary ultrasonic vibration assisted EDM machine tool, the errors caused by the main moving parts of the machine tool were analyzed. The structure relationship is described by multi-body system theory, and the geometric error model is deduced by homogeneous coordinate transformation method. The aim was to improve the overall accuracy, reduce the cost, trace the error source, and get the corresponding relationship between the basic geometric errors and the precision parameters of the related parts. Under the requirement of ensuring the overall accuracy, an optimization function was established that minimizes the cost and relaxes all accuracy parameters to the greatest extent. Finally, MATLAB and its genetic algorithm toolbox was used for analysis. Verification shows that this model can properly reduce the cost on the premise of ensuring the accuracy.

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.

Simulating human–machine coupled model for gait trajectory optimization of the lower limb exoskeleton system based on genetic algorithm

The lower limb exoskeleton robot is capable of providing assisted walking and enhancing exercise ability of humans. The coupling human–machine model has attracted a lot of research efforts to solve the complex dynamics and nonlinearity within the system. This study focuses on an approach of gait trajectory optimization of lower limb exoskeleton coupled with human through genetic algorithm. The human–machine coupling system is studied in this article through multibody virtual simulation environment. Planning of the motion trajectory is carried out by the genetic algorithm, which is iteratively generated under optimization of a set of specially designed fitness functions. Human motion captured data are used to guide the evolution of gait trajectory generation method based on genetic algorithm. Experiments are carried out using the MATLAB/Simulink Multibody physical simulation engine and genetic algorithm-toolbox to generate a more natural gait trajectory, the results show that the proposed gait trajectory generation method can provide an anthropomorphic gait for lower limb exoskeleton device.

Orderly charging strategy of battery electric vehicle driven by real-world driving data

The work preprocessed the real-world driving data of 1000 battery electric vehicles (BEVs) in Zhengzhou, China. Then a scheduling model of electric vehicles on time dimension was established based on the processed data. The mathematical model could meet the operation requirements of grid side and user side. The grid-side optimization minimized the system’s equivalent load fluctuation, and the user-side was optimized to maximize the charging capacity of electric vehicles. The mathematical model was solved by the genetic algorithm toolbox in Matlab software. Besides, we obtained the quantity distribution of BEV access to the power grid, parking time distribution, parking duration distribution and initial state of charge (SOC) distribution at the beginning of charging by analyzing the real-world driving data. These distribution curves were used to obtain the driving and charging habits of BEV drivers. By comparing the optimized orderly charging strategy with the random charging, in the case of meeting the user’s demand for charging power, the peak and valley difference and the equivalent load fluctuation of the power grid were significantly reduced by 22 and 22.7%, respectively. It greatly improves the security and economy of the grid.

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.

Optimal Pendulum Tuned Mass Damper Design Applied to High Towers Using Genetic Algorithms: Two-DOF Modeling

High and slender towers may experience excessive vibrations caused by both wind and seismic loads. To avoid excessive vibrations in towers, tuned mass dampers (TMDs) are often used as passive control devices due to their low cost. The TMDs can absorb part of the energy of vibration transmitted from the main structure. These devices need to be finely tuned in order to work as efficient dampers; otherwise, they can adversely amplify structural vibrations. This paper presents the optimal parameters of a pendulum TMD (PTMD) to control the vibrations of slender towers subjected to an external random force. The tower is modeled as a single-degree-of-freedom (SDOF) mass–spring system via an assumed-mode procedure with a pendulum attached. A genetic algorithm (GA) toolbox developed by the authors is used to find the optimal parameters of the PTMD, such as the support flexural stiffness/damping, the mass ratio and the pendulum length. The chosen fitness function searches for a minimization of the maximum frequency peaks. The results are compared with a sensibility map that contains the information of the maximum amplitude as a function of the pendulum length and the mass ratio between the pendulum and the tower. The optimal parameters can be expressed as a power-law function of the supporting flexural stiffness. In addition, a parametric analysis and a time-history verification are performed for several combinations of mass ratio and pendulum length.

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.

Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach

In the recent decade, thermal drilling is becoming popular in aerospace and automobile industries because of its unique advantages over conventional twist drilling process. Surface finish of the thermally drilled hole along with its bushing, is a major concern in all crucial applications and it is worthy of investigation. In the present study, the surface roughness of the thermally drilled hole on galvanized steel is predicted and then optimization is carried out, employing an integrated adaptive network-based fuzzy inference system (ANFIS) and genetic algorithm (GA) approach. Experimentation is based on Taguchi L27 orthogonal array and significant parameters such as spindle speed, angle of tool and workpiece thickness are varied in different levels keeping feed rate as constant. Using the experimental results, an ANFIS model is developed for prediction of surface roughness. An objective function is then formulated on minimization of surface roughness with the help of predicted results of the ANFIS model. Then this objective function was imported into GA toolbox of MATLAB software to optimum values of surface roughness of thermally drilled hole. High degree of closeness is observed between the experimental and predicted results. It is also found that the spindle speed and angle of tool play a significant role on the surface roughness of drilled holes in galvanized steel.

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.

Hybrid Seven-bar Press Mechanism

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

Service Location Optimization Model for Improving Rural Emergency Medical Services

Approximately 35,000 fatalities are attributed to accidents on U.S. highways each year and more than half of them occurred in rural areas. With such a high percentage of fatalities, rural areas are in critical need of timely and reliable Emergency Medical Services (EMS). EMS provide important prehospital care to victims before they are transferred to a hospital. After an accident occurs, the time it takes for victims to receive care from EMS is crucial to their survival. Compared with urban EMS, rural EMS face multiple challenges. One of them is how to properly site EMS stations to provide cost-effective services in rural areas. The goals of this paper include analyzing the spatial patterns of EMS station and incident locations, and optimizing rural EMS station locations. The data were collected from South Dakota, a rural state. This dataset was used to perform spatial analysis and to develop and evaluate an EMS location optimization model. The location optimization model aims to maximize the rural EMS coverage while taking service equity into consideration. The model was solved by a genetic algorithm toolbox in R. The proposed model provides an important and practical tool for rural EMS officials to select new EMS stations or relocate existing stations to improve service performance under budget and resource constraints.