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In the present analysis 15 experiments were performed in conjunction with the Box-Behnken architecture matrix based on the machining parameter's effect, like spindle speed, feed rate, and cutting width., A surface roughness mathematically framework was designed using the surface reaction methods of this model to aid a genetic algorithm. Which is used to decide the optimum machining parameters. Response surface methodology has been used in this paper due to certain advantages as compare to other methodology such as it needs fewer experiments to study the effects of all the factors and the optimum combination of all the variables can be revealed. Finally, a genetic algorithm was used to determine the optimum setting of process parameters that maximize the rate of content removal. The best surface roughness response value obtained from single-objective genetic algorithm optimization was 1.19 μm.

In the present work, the Adaptive-Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.

The study deals with the design and optimization of external and internal geometry of micro-wind turbines blades. A specified objective function which consists of the power coefficient and the starting time was defined and the genetic algorithm optimization technique in conjunction with the blade-element momentum theory was adopted to find the geometry of the blades including the distributions of the chord, the twist angle and also the shell thickness. Moreover, the allowable stress of the blades was considered as a constraint to the objective function. Results show that a reasonable compromise is achievable such that the starting time of the blades reduces noticeably in return for a small drop in the power coefficient. The significant improvement of the hollow blades over the solid ones indicates that the power coefficient and the starting performance could be improved through the appropriate distributions of the considered decision variables, i.e. the chord, the twist angle and also the shell th...

Design of a Small Diesel-Battery Submarine (SDBS) has been carried out using Multi-Objectives Genetic Algorithm optimization combined with Analytical Hierarchy Process to meet the operation requirements. The objective attributes for this optimization are cost and effectiveness. The product of this optimization is a series of cost-effectiveness frontiers in the scattered graph which are used to select alternative designs based on the Indonesian Navy's preference. It is found that the Length of the submarine is 28 m with Diameter 4 m. The sprint power of 20 knots determines the motor power while the endurance power of 5 knots determines the battery capacity.

A general computational alloy design approach, based on thermodynamics and thermokinetics and coupled with a genetic algorithm optimization routine, was applied to the design of novel creep martensitic resistant steels. The optimal alloy suggested by the model has a high density of barely coarsening MX carbonitride precipitates. The model yielded precise values for the concentrations of the 10 alloying elements considered. The model alloy was produced on a 10 kg lab scale. Samples of the new alloy of one of the best commercial martensitic steels on the market P92 were subjected to a high aging temperature of 923 K (650 °C) for times up to 1000 hours. The microstructure of the new alloy in the as-produced state as well as after 1000 hours exposure has all the intended features as predicted by the model. The coarsening rate of the MX rate carbonitrides was substantially lower than that of the precipitates in the P92 steel. The very low coarsening rate explains the superior hardness at very long exposure times.

The present study investigates the use of HVAC and solar heating systems inside residential zones using a combination of direct numerical modeling and genetic optimization method in different climates of Iran. The direct numerical modeling is capable of calculating temperature field inside buildings. Then, direct modelling algorithm is used to determine energy consumption rate of the building in the different climates of Iran. The results of direct numerical modelling are checked against those of carrier and energy-plus software. The results show good accordance with those of numerical simulations presented in the open literature.

This paper deals with the problem of metal-oxide surge arrester (MOSA) diagnostics. The proposed methodology is based on a genetic algorithm optimization technique. The genetic algorithm (GA) is applied to determine the parameters of MOSA equivalent model. According to the assessed parameters, which change during the lifetime of the MOSA, the MOSA condition can be determined. For this purpose, the realistic measurement data of the operating voltage and the leakage current is used. Additionally, computer simulations are conducted in order to demonstrate the influence of the operating voltage harmonics on the algorithm performance and dynamics. The proposed algorithm is tested on realistic MOSA in laboratory conditions.

The design of a new compact band-pass filter is proposed, which is based on the microstrip composite right- and left-handed transmission-line (CRLH-TL) structure. Particularly, the interdigital coupled (IDC) lines of the CRLH geometry are proposed to be parted by inserting open stubs to meet the specifications on the passband. In addition, there is another pair of stubs to complete the design in a limited space. These are considered in the TL-based analysis and the design parameters are calculated by genetic algorithm optimization. The measurement is shown to be acceptable and agreeable with the circuit and electromagnetic field simulations. In addition, the zerothorder resonance (ZOR) phenomenon is verified.

In this paper, an adaptive fuzzy PID controller based on genetic algorithm is designed. Brushless DC motor uses double closed loop control system. The adaptive fuzzy PID controller based on genetic algorithm is applied to outer ring speed ring, and PI controller is applied to inner ring. The simulation results show that, the designed brushless DC motor control system based on genetic algorithm optimization has a short rise time and no overshoot, small steady-state error and other advantages. And the system has strong robustness and adaptability.

Taking the lower arm of DK7725 WEDM Taper Machine as the research object, this paper uses the ANSYS Workbench collaborative simulation environment to analyze equivalent stress and deformation of the lower arm in the different working conditions, find the weak part of Lower Arm, analyze static structure of the lower arm, and improve lightweight design. According to the characteristics of Taper Machine overall structure and optimization variables, using the Design of Experiments (DOE) method, this paper combines the response surface method (RS), goal driven optimization method (GDO) with multi objective genetic algorithm optimization method (MOGA) with the candidate schemes, extractes candidates from the calculation program, insert the candidates as the design points into the whole optimization calculation. By controlling parameter allowable error, this experiments obtains the best optimal solution. The results show that if the mass of the lower arm is decreased 13.28%, the lower arm has stable dynamic performance,and get good optimization effect.

Multi-car elevator system is a new technique which can reduce the occupied area and improve the transport efficiency in the high-rise and super high-rise buildings. It is remain to be studied in both the control strategy and scheduling method compared with the traditional single elevator group control system, In order to improve the utilization rate and shorten the waiting time, the particle swarm algorithm PSO and GA genetic algorithm optimization control method are studied aiming at the rationality of scheduling strategy of the rationality of elevator. Combining the advantages of two algorithms proposed a novel optimal scheduling algorithm for PSO-GA and carried on simulation analysis, the experimental results demonstrate the effectiveness of the new method of improved PSO-GA.

Based on the genetic algorithm (GA), a novel type of parameters identification method on battery model was proposed. The battery model parameters were optimized by genetic optimization algorithm and the other parameters were identified through the hybrid pulse power characterization (HPPC) test. Accuracy and efficiency of the battery model were validated with the dynamic stress test (DST). Simulation and experiment results shows that the proposed model of the lithium-ion battery with identified parameters was accurate enough to meet the requirements of the state of charge (SoC) estimation and battery management system.

A new cold-formed steel corrugated shear wall seismic force resisting system has been recently developed by Tipping Mar and Associates of Berkeley, California. The seismic performance quantification of buildings with the system requires non-linear dynamic analysis, where the shear wall – to reduce calculation demands – is modeled by cyclic strut element. The paper presents the model development and calibration to test results. In lack of monotonic test results, monotonic backbone curve is estimated by non-linear static analysis. Cyclic deterioration parameters are determined by genetic algorithm optimization method. Standard model that is applicable to the various shear wall configurations is developed.

The research in this paper is intended to recommend an approach for adequate prediction of the properties of iron-based alloys for a preset composition and mode of heat treatment. Stages of creation (design), testing, production and deployment of high strength alloy steel, include the specification of the chemical composition, the parameters of the mode of thermal treatment and the final mechanical properties. The steel for its components and features for heat treatment is a technological object and therefore it is possible to apply for it an approach for modeling the properties and optimizing the composition depending on the particular application. The procedure of a reasoned elaboration of the chemical composition by the number and the amount of alloying elements is relatively new related to the pursuit of the final mechanical properties. The practical results are applicable and they can be used for:  the design of more efficient compositions in terms of the expensive alloying elements while maintaining the basic properties above a given threshold,  evaluation of the technological cost of equally applicable technological variants of varying degrees of doping steel,  determination of a rational representative of a certain class of materials best suited to the requirements previously set (most often controlled properties) among the rest of the class.

A genetic algorithm (GA) model for water distribution system (WDS) operation has been developed, optimizing pumping by time-based scheduling and tank trigger levels. An important focus was the minimization of operational greenhouse gas (GHG) emissions, in conjunction with operational economic cost, to provide a comprehensive solution to the pumping problem. Various possible future energy scenarios have been investigated to determine the effect of varying GHG emissions factors on the optimal operational decisions for WDSs. The interface developed in this research allows users to apply the optimization algorithm to a variety of water networks with full customization of inputs and parameters.

Piercing manufacture of seamless tubes is the process that pierces solid blank into tube hollow. Piercing efficiency and energy consumption are the important indexes in the production of seamless tubes. Piercing process has the multivariate, nonlinear, cross-coupling characteristics. The complex factors that affect efficiency and consumption make it difficult to establish the mechanism models for optimization. Based on the production process, this paper divides the piercing process into three parts and proposes the piercing efficiency and energy consumption prediction models based on mean value staged KELM-PLS method. On the basis of mean value staged KELM-PLS prediction model, the minimum piercing energy consumption and maximum piercing efficiency are calculated by genetic optimization algorithm. Simulation and experiment prove that the optimization method based on the piercing efficiency and energy consumption prediction model can obtain the optimal process parameters effectively and also provide reliable evidences for practical production.

Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

In this paper, we propose a hybrid case-based reasoning (CBR) system for predicting the construction cost of high-rise buildings at the preliminary design stage. First, the extracted cost factors (CFs) of a high-rise building were shown to significantly improve the cost estimation system’s performance. For developing a CBR system, a hybrid approach that combines CBR with genetic algorithms (GAs) for cost estimation was adopted. Genetic algorithms were used for optimized weight generation and applied to real project cases. Additionally, this paper proposes the identification of an alternative similarity score measurement formula. The proposed formula evaluates the contrast between the alternative case matching approach and the classical formula in a scenario involving the use of cost factors describing a case. The results indicate that the proposed GA-based CBR system can consistently reduce errors and potentially be useful to owners and contractors in the early financial planning stage. Accordingly, it is expected that the developed CBR system would provide decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling the cost.

It will be face some problems about the complicated reaction mechanism, environment uncertainty, serious nonlinear in nitric acid process .a method of creating steady-state model of nitric acid process using neural network. and used genetic algorithm to optimize parameter on neural network model. The result can provide reference for analyzing and optimizing the parameters of nitric acid process.

in the greenhouse, the the thick skin melon tends to be more easily infected by some diseases. The traditional forecasting model disease convergence speed is slow and limited by the minimum, easily. This study, based on the BP neural network but to optimize it and introduce the genetic algorithm, through the local searching nearby the global optimal solutions, overcomes the local minimum value and convergence speed defects of traditional neural network with genetic algorithms global search ability. The experimental datas simulative analysis by Matlab shows that the thick skin melon's disease predicting error has been reduced significantly after the introduction of genetic optimization algorithm and has obtained an ideal fitting result.

In cupola melting process, the temperature of molten iron is an important indicator of the quality of cast iron. Its difficult to optimize the design because of the varicosity of influencing factors in cupola melting process. This article established a BP neural network model to forecast the temperature of molten iron in cupola melting process, thus use the genetic algorithm to optimize the model. Comparing the average errors of the temperature of molten iron before and after optimization, it indicated that the BP neural network model using genetic algorithm optimization forecasted the actual situation in cupola melting more accurately.

A multi-objective optimization-based computational approach to nondestructive evaluation of damage in structural components, and more generally in solid continua, is discussed and numerically evaluated. The multi-objective approach provides a substantial improvement in the capabilities to traverse the optimization search space to minimize the measurement error and produce accurate damage estimates. Through simulated test problems based on the characterization of damage in structural steel components, including internal pipe surface geometry as well as material loss within a plate structure utilizing steady-state dynamic measurements of outer surface displacement, a multi-objective genetic algorithm optimization approach is shown to provide substantial computational improvement over single-objective strategies. The multi-objective approach consistently and efficiently produces more accurate characterization results in contrast to equivalent single-objective strategies. More importantly, the multi-objective approach is shown to exhibit consistently better tolerance to test measurement noise and measurement sparsity. Moreover, the multi-objective strategy was found to provide improved diversity in the solution estimates for ill-posed problems, which is an important step leading to insight into the necessary changes to the testing or parameterization to subsequently produce more accurate and unique solutions to such inverse characterization problems.

Purpose – The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to demonstrate that CO2 emissions could be reduced at a reasonable cost. The paper also investigates the trade‐offs between cost and reliability of water distribution networks.Design/methodology/approach – An existing genetic algorithm optimization tool is customized in this research to perform multi‐objective optimization with various objectives and constraints. The developed model is demonstrated using a benchmark water distribution network.Findings – The results from this research suggest that CO2 emissions from water distribution networks could be reduced at a reasonable cost by choosing better objectives during the design stage. High system reliability could also be ensured for the lifetime by paying reasonable additional cost. This research presents various design alternatives for an engineer to choose from.Research lim...

4E analysis and multi-objective optimization for a combined cycle power generating unit with a Multi-stage Flash (MSF) desalination unit are investigated in this paper. The first objective function was considered as the sum of investment and operational costs as well as penalty for producing NOx emissions. The second objective function was the cycle total amount of exergy destruction. Genetic algorithm optimization technique was applied to obtain the optimum values of design parameters such as Heat Recovery Steam Generator (HRSG) drum pressure, pinch point temperature in HRSG, top brine temperature in MSF, last stage temperature of MSF and number of MSF stages. Also the effects of gas turbine part load, as well as ambient temperature and fuel cost changes on the optimal values of design parameters were analyzed.

Micro turning is a process by which micro components can be produced.This paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Machining is done in DT-110 integrated multiprocessor micro machine tool.Micro turning is carried out with full factorial experiments with various combinations of cutting parameters such as speed (25,31, and 37 m/min), feed (5,10, and 15 μm/rev) and depth of cut (30,50 and 70 μm). For every set of experiments, the output parameters such as the tool wear and the surface roughness are measured. Non-linear regression model is used to represent relationship between input and output variables and a multi-objective optimization method based genetic algorithm is used to optimize the cutting parameters in turning process such as cutting speed, feed and depth of cut. Two conflicting objectives such as tool wear and surface roughness are simultaneously optimized.

An aerodynamic shape optimization methodology based on Genetic Algorithm and Blade Element Momentum theory is developed for rotor blades of horizontal axis wind turbines Optimization studies are performed for the maximization of power production at a specific wind speed, rotor speed and rotor diameter. The potential flow solver with a boundary layer model, XFOIL, provides sectional aerodynamic loads. The sectional chord length, the sectional twist and the blade profiles at root, mid and tip regions of the blade are taken as design variables. The blade sections may be defined by the NACA four digit airfoil series or by arbitrary airfoil profiles defined by a Bezier curve. The sectional flow computations required by Genetic algorithm, which is inherently parallel, are performed in a parallel computing environment with 512 cores. Message Passage Interface (openMPI), is employed in parallel computations. Validation studies are first performed.

Th is paper presents Genetic A lgorith m (GA) optimization of Log-Periodic Dipole Array (LPDA). Considering the fact that, an LPDA can effectively be used for different wireless communication systems such as WiMAX, GSM-I, GSM-II and WiFi, respectively. GA optimization is applied to the physical size of the antenna and on its gain. The proposed design is compared with the conventional LPDA design in terms of length, diameter and spacing between the dipole elements. Presented results show that, the proposed optimization approach can significantly imp rove the antenna gain and reduced its size. The size of the antenna is reduced up to 12% as compared to the conventional design. Also, the gain is imp roved fro m 9.1, 9.5, 9.2 and 8.5 d B to 10.7, 11.2, 9.9 and 9.1 d B for the desired co mmun ication bands. The reduction in size and increment in gain make LPDA an attractive choice for wireless applications.

The main goal of this paper is to describe a process developed to reduce the cost of composite and steel semi-rigid endplate joints. The developed process is based on an adequate selection of the most significant variables that controls the joints structural performance to obtain effective cost solutions. This procedure was based on the use of a genetic algorithms producing trustable results in terms of economy and efficiency, with a reduced computational effort. Four set of examples involving flush and extended endplate joints are presented to illustrate the developed optimization procedure. These joints were initially considered to be made only with steel members. In a subsequent phase equivalent composite joints were also considered to enable a direct comparison of the these alternative typologies. The global costs associated to the adopted joints were evaluated taking into account parameters like: endplate dimensions, number of adopted bolts, bolt diameter among others. The performed examples validated and proved the viability of using GA to optimize composite and steel semi-rigid joints.

External grounding grid is an effective measure to reduce grounding resistance of grounding grid. In order to meet the requirements of the grounding resistance in the most economical way, it is important to determine the distance and the area of the external grounding grid. Generally speaking, when the area is larger, the grounding resistance is smaller. When the distance of the external grounding grid increases, the grounding resistance decreases firstly and then increases. Therefore, there is a minimum of the external grounding grid area and the optimal distance to meet the requirements of the grounding resistance within the constraints. Also means to get the best economic design scheme that minimum material consumption and construction of the external grounding grid. In this paper, we establish the objective function of the minimum grounding resistance based on the area and the distance of the external grounding grid using the method of grounding grid unequal potential model. Through optimization theory and genetic algorithm optimization, the optimum parameters of the distance and the area size of the grounding grid are obtained. Provide important basis for the design of the grounding grid economically.

The collector is an important component in a Gyro-Travelling Wave Tube. The performance influences the capability of tube. The curved geometry is employed to average the dissipated power distribution. Compared to the previous cylindrical geometry, the maximum dissipated power is reduced from 0.83 kW/cm2 to 0.396 kW/cm2. The temperature on inner wall is reduced from 227°C to 116°C. The capability of collector is improved. Also the stability analysis is done, and it shows the curved geometry can operate stably.

In the present paper an ‘in-house’ genetic algorithm is described and applied to an optimization problem of improving the aerodynamic performances of an airfoil through upper surface morphing. The results of the optimization of the flow behavior for the airfoil morphing upper-surface problem are validated with experimental transition results obtained with Infra-red Thermography for the CRIAQ MDO 505 wing tip demonstrator, proving that the 2D numerical optimization using the ‘in-house’ genetic algorithm is an important tool in improving various aspects of a wing’s performances, 34th AIAA Applied Aerodynamics Conference, June 13-17, 2016, Washington, D.C.

This paper addresses the problem of finding a set of optimal foot and spine trajectories for a biologically inspired quadruped robot in order to obtain energy optimal and fast locomotion in forward direction. Based on the previous work of finding optimal foot trajectories we now extend the scope of problem by investigating the effects of an added 2DOF actuated spine on the characteristics of robot locomotion in comparison with rigid spine. To tackle this problem a multiobjective genetic optimization algorithm was used. Purpose of the optimization procedure is to give a better insight in which of the two spine configurations is more beneficial to studied bound and trot gaits and how to design a control strategy that utilizes the spine in an effective way.

Application of multirotor flying robots to aerial photography and similar tasks has been steadily rising. A new configuration of octorotor flying robot is proposed in this study that offers many advantages over traditional designs. Euler-Lagrange formalism is used to develop dynamical model of the vehicle. The model is experimentally validated using laboratory data and a recursive modification method based on genetic algorithm optimization. It is shown that derived model can provide a reliable estimate of the robot's dynamics and characteristics.

Head injury of pedestrian is the most common and fatal cause of mortality in vehicle-to-pedestrian crash. And the engine hood is most likely to cause harm to pedestrian head. Efforts to improve engine hood design, which minimize the head injury of pedestrian in vehicle-to-pedestrian crash, are becoming more and more important. In this study, an approximate model of hood thickness for three targets: HIC, mass and modality, is established. In order to meet the requirements of lightweight and reducing vibration and noise, approximate models iterate by the NSGA-II genetic optimization algorithm, and select the Pareto optimal solutions for thickness optimization. At last the study re-simulates the collision between pedestrian head and hood to verify the reliability of the obtained optimization results.

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

In steelmaking plant, the process times of machines change frequently and randomly for the reason of metallurgical principle. When those change happen, the plant scheduling and caster operation must respond to keep the optimal performance profile of plant. Therefore, the integration of plant scheduling and caster operation is a crucial task. This paper presents a mixed-integer programming model and a hybrid optimized algorithm for caster operation and plant scheduling, which combine the genetic algorithm optimization and CDFM process status verification. Data experiments illustrate the efficiency of our model and algorithm.

Reactive power optimization in the power system m tends to maintain good voltage profile by improving the voltage quality other than decreasing the power loss. This pape er presents an improved Genetic algorithm (GA) approach for voltage stability enhancement. The proposed technique is s based on the minimization of the maximum of L-indices o of load buses. Generator voltages, switchable VAR sources and d transformer tap changers are used as control variables. A case study is made on all the optimization variables mentione ed and the effect on generators reactive power output is a analyzed. The comparison of two optimization techniques is explained in detail. The results obtained for the IEEE-24 bus p power system had indicated that the GA not only improves the v voltage stability but also reduces the effect on generators for r the Reactive Power.

An improved oil price forecast model that uses support vector machine (SVM) was developed. The new model, called the GA-SVM forecast model, is based on genetic algorithm (GA) optimization parameters. In traditional SVM models, penalty factor C and kernel function parameter σ are generally dependent on experience. These empirical parameters are difficult to accomplish the price data's change. Therefore, we used GA to optimize the parameter selection methods of SVM in accordance with training data, and improved SVM forecast precision. To verify the validity of the model, we selected and analyzed the Brent oil stock price data from 2001/12/27 to 2011/10/30. Data for 2009/07/30 to 2011/07/22 were designated as training data set, and those for 2011/08/08 to 2011/08/17 were employed for testing. Results show that the forecast efficiency of GA-SVM was better than that of traditional SVM.

This paper presents a safe and economic grounding system design for the substation of Ain El-Melh located in M'Sila city (Algeria) using genetic algorithm optimization (GAO). The main objective is to minimize the cost function of the considered grounding system in accordance with the security restrictions required by the ANSI/IEEE Std. 80-2000. A new mathematical model has been proposed for the cost function. This latter includes number of conductors, conductor dimension, number of rods, length of rods and total area of revetment. The study results have showed the effectiveness of the established GAO algorithm in designing of grounding system in economic view. A good accordance has been obtained when comparing the results found by GAO algorithm and the calculation code CYMGrd ones.

This paper proposes a modified genetic algorithm (GA) for optimization of water distribution systems. A method of dynamically expanding the pipe-choice-table selections and reducing the number of decision variables is introduced, which occurs during a GA run. On the basis of the progressive selection, an initially reduced choice table for each decision variable is allowed to dynamically expand, and then the number of decision variables is gradually reduced. This process enables the GA search to concentrate on promising regions of the search space. The dynamically expanding choice-table genetic algorithm (GADECT) has been applied to a benchmark case study, the New York Tunnels Problem. The results obtained show that the GADECT yields a superior performance in terms of solution quality and computational efficiency.

In this paper we propose an improvement of methods for adaptive selection of frequency bands containing transients which indicate the presence of the dominant resonant modes of rolling bearing faults using niching genetic algorithm optimization. The main aim of this approach is to diagnose the condition of the bearings and to be able to recognize faults on various parts of bearings and possible combinations of faults. Because the vibration signals corresponding to faults on bearings are typically transients with a wide frequency range occurring around the excited mechanical resonant modes and drowned in the acquired vibration signals, it is necessary to emphasize these excited transients using a matched bank of filters. The dominant resonant modes of a bearing and the system modes produced from fault source are usually unknown, and so there is a need for robust global search methods able to deal with non-linear problems with multiple optima. Instead of applying an optimization method repeatedly for every optimum, non-dominated extensions of the genetic algorithm can be applied only one time to find and maintain multiple optimal solutions. The efficiency of the proposed approach – niching genetic algorithm with fitness sharing – was evaluated using vibration signals acquired on four tapered roller bearings with defined combinations of seeded faults.

This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts robust design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multiobjective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. This paper involves engine modelling in 1D software simulation environment, GT-Power. The GT-Power model is run simultaneously with mode Frontier to perform multiobjective optimization.

PurposeThe purpose of this paper is to present a new approach for the optimal design of protective current transformers (CTs). The proposed technique is applied to the design of typical CTs and results are compared with traditional CT design method and genetic algorithm optimization algorithm.Design/methodology/approachThe aim of CT design is to make current measurements more accurately, particularly for the high fault currents which arise in short circuits, and more efficient CT in terms of both size and cost. The present work formulates these objectives as an optimization problem to be solved by particle swarm optimization.FindingsSimulation results demonstrate the effectiveness of this technique in optimizing the CT design parameters. The designed CTs have smaller measurement errors compared to standard values and respond well to high fault currents. Manufacturing costs have also been reduced.Originality/valueIn addition to improved efficiency, the benefits of this method are its treatment of CT design in terms of an equivalent circuit and design parameters. The proposed algorithm also extends the linear operation area of the CT and guarantees its good response to high fault currents that may occur in power systems.

Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. Genetic algorithm optimization (GAO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper applies two GAO-based approaches to multi-objective engineering design and finds design variables through the feasible space. To demonstrate the utility of the proposed methods, the multi-objective design of an I-beam will be presented.

CFD-simulations were conducted to simulate the flowfield around a bobsleigh. A simplified crew model of the pilot and the brakeman of a 2-man bobsleigh was generated. The model was used to perform the fully automated optimization of the crew posture and position. Furthermore, a genetic optimization algorithm from the software package DAKOTA was used in order to search for the optimum crew position and posture. The considerable effect of the crew position and posture on the aerodynamic drag was verified and an optimum crew posture and position was found that deviates from a maximum ducking posture of the brakeman.

In this article, load frequency control of an interconnected power system is achieved by two methods. The first is based on the classical h∞ control method and is subjected to linear matrix inequalities. The second uses a proportional-integral controller that is tuned by genetic algorithm optimization and is also subjected to the same linear matrix inequalities in order to obtain robustness against disturbances. Both controllers are tested on a two-area power system with three scenarios of load disturbances, as well as on power systems in a deregulated environment to demonstrate their robust performances.

In this article, the tuning of feedback gains of a variable structure load frequency controller by genetic algorithm optimization of selected performance indices is presented. The load frequency control of a single- and multi-area power system has been carried out. The effect of generator rate constraints has been studied. The system performance against step load variations has been simulated and compared with the conventional trial-and-error method. The results show that the system dynamic performance has been improved, the control effort has been reduced, the controller is robust against large disturbances, and the system is insensitive to wide parameter variations.

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.