Micro-genetic Algorithm Research Articles

Investigation of sensitivity analysis and compensation parameter optimization of geometric error for five-axis machine tool

To improve the accuracy of five-axis machine tool with a swiveling head, an approach for optimizing compensation values taking into account the sensitivity of position-independent geometric error is proposed in this paper. At first, the synthetic volumetric error model of five-axis machine tool has been established with homogeneous transformation method and multibody system theory, which describes the effect of geometric error components on position and orientation error vector intuitively. Second, the probability distributions of geometric errors in workspace of machine tool are sufficiently taken into account in order to overcome the defects of the analysis at certain locations, and then, global quantitative sensitivity analysis is introduced for determining the effect of each geometric error on precision of machine tool. Next, the optimum values are obtained by multiobjective quality loss and precision robustness trade-offs based on archive-based microgenetic algorithm (AMGA) for geometric error compensation. Finally, the geometric error compensation experiments were carried out, and the results show that the accuracy of measuring trajectories are improved significantly, which reaches 73.7% after error compensation with optimum values based on sensitivity analysis. Hence, the proposed methodology of analysis and compensation are effective for analyzing the effect of geometric errors and improving the precision of machine tool.

Aperiodic Multilayer Graphene Based Tunable and Switchable Thermal Emitter at Mid-Infrared Frequencies

Graphene has attracted an explosion of interest for photonic applications as it provides a degree of freedom to manipulate electromagnetic waves. In this work, we propose a new one-dimensional system composed of multiple graphene and hexagonal boron nitride (hBN) with varying aperiodic thicknesses in between the graphene layers. A hybrid optimization method, consisting of a micro-genetic global optimization algorithm coupled to a local optimization algorithm, indicates that absorption efficiency close to 100% can be achieved at very narrow frequency range under normal incidence. This efficiency can be significantly decreased by tuning the chemical potential and the corresponding carrier density in graphene. It thus enables a promising way to design electrically controllable thermal emitter. Fig. 1(a) shows the proposed device structure composed of 23 graphene layers and 22 alternating layers of hexagonal boron nitride (hBN) insulator, which sandwiches between two thick silicon carbide (SiC) layers. The hBN has two-dimensional atomic structure similar to graphene and has been proposed as its promising complementary insulator layer. The ambient temperature of 873 K is assumed, in which the semi-infinite tungsten substrate creates the thermal emission of black body radiation with maximum emission at infrared range. Since the tungsten substrate is taken to be semi-infinite, the transmittance is identically zero (Absorption = 1-Reflection) and the calculated absorption can be equated to emittance because of Kirchhoff’s second law and conservation of energy. The Kubo formula is used to extract the optical conductivity and consequently the refractive index of graphene while the experimental data is used for the wavelength-dependent indices of refraction of other materials. Fig. 1(b) shows the normalized power radiated per unit area and unit wavelength by the structure. In order to find the optimized structure with narrowband thermal emittance, the chemical potential initially set at the Dirac point of graphene layers (µg=0eV), thus introducing minimum insertion loss or signal attenuation. It can be seen that the proposed device can exhibit narrowband infrared emittance, leading to most prominent emission at λ=3340 nm with narrower wavelength range than black body curve. This wavelength corresponds with the λ/(4nSiC) of 538nm SiC layer, matched with the maximum thermal emission of black body radiation at T = 873 K. This indicates promising application of the proposed structure in design of thermophotovoltaic, in which the frequency of thermal radiation must be matched with the band gap of semiconductor. After obtaining the optimized structure, the dimensions are fixed and the carrier density in graphene is increased by tuning the chemical potential to larger values. It can be seen in Fig. 1(b) that the field effect tuning of carrier density in graphene reduces the normalized thermal emission from 100% at µg = 0eV to 64%, 43%, and 16% corresponding to the chemical potentials of µg = 0.4eV, 0.6eV, and 1.0eV, respectively. The intensity, width and the position of wavelength range is widely tunable, leading to electronic control of thermal emission. As the carrier density is increased, there is a decrease in intensity and a spectral shift toward smaller wavelength. When a bias is applied, two-dimensional electron gases are induced in the graphene layers, resulting in higher contribution of intraband transitions especially at high temperature of black body emitter. This manipulates the optical conductivity of graphene and alters the constructive and destructive interference of thermal incidences and reflections from the graphene/hBN layers, leading to different intensity, width, and position of thermal emittance. In this work, we present a 1D system composed of graphene/hBN heterostructure as a tunable and switchable thermal emitter. While the black-body thermal emission is a broadband and isotropic radiation, the proposed structure may contribute toward the realization of narrowband-wavelength selective thermal emitters with switchable intensity for sensing applications. Figure 1

Optimization of interconnected absorption cycle heat pumps with micro-genetic algorithms

Production of hot water in district heating plants needs to be adjusted on a day-to-day basis to match the expected demand and availability and prices of energy resources. However, such plants are often highly nonlinear and complex. It therefore makes sense to attempt to automate optimization of the operating conditions within the physical boundaries set by the plant equipment. In this work, we investigate the use of micro-genetic algorithms to achieve constrained global set-point optimization based on a dedicated simulation model. The model is based on a real district heating plant consisting of four interconnected LiBr–water based absorption cycle heat pumps primarily driven by a geothermal reservoir and wood chip boilers. Various scenarios are considered, and it is found that the proposed genetic algorithm is able to find combinations of valid set-points that provide savings of several percent of current operating costs compared to a baseline scenario in reasonable computation time.

Optimal design of filament wound truncated cones under axial compression

In this study, filament wound truncated cones under axial compression are with the objectives of minimizing the total weight and maximizing the failure load, which is defined as the minimum of the buckling and the first-ply failure (FPF) loads. The numerical results are obtained using an axisymmetric degenerated shell element based on a refined first-order shear deformable shell theory and a 2D degenerated shell element is used for verification purposes. It is shown that, FPF is more critical than buckling for thicker cones with lower cone angles.Optimal designs, where FPF and buckling are imposed as design constraints, are presented for filament wound cones using Micro-Genetic Algorithms. The results show that, using more layers having different winding angles has negligible influence on the failure load and the optimal design is not FPF critical for moderate levels of axial compression. The influence of the rotational boundary conditions on the optimal failure load is also demonstrated.

Classification of breast tissues into mass and non-mass by means of the micro-genetic algorithm, phylogenetic trees, LBP and SVM

This work has the objective of developing a methodology for the classification of regions of interest (ROI) extracted from mammograms into masses and non-masses. To this end, we conducted a comparative study by combining several techniques in the various stages of the problem. We compared several texture-based techniques for the classification of ROIs. This study combines image processing, phylogenetic trees (PT), local binary patterns (LBP), support vector machines (SVM) and the micro-genetic algorithm (GA). The analysis of texture is performed, either through the combination PT/LBP or with grey levels, to compute the taxonomic diversity () and taxonomic distinction (*) indexes extracted from sub-regions (circular, circular crown, internal mask, external mask and the combination of internal and external mask) of an ROI. A GA is used to estimate the best phylogenetic weights. Then, its results are compared with the results achieved with the use of PT only. We also analyse the behaviour of the methodology when using the ROIs with and without enhancement. This enhancement consists of the application of a mean filter and the contrast-limited adaptive histogram equalisation (CLAHE). PTs and SVM were used to perform the selection of features. To evaluate the performance of the methodologies under analysis, we used the following metrics: sensitivity, specificity, accuracy and area under the receiver operating characteristic (ROC) curve (). Sensitivity and specificity measure the efficiency of the classifier at the correct detection of positive (masses) and negative (non-masses) cases, respectively. Accuracy measures the performance of the classification in both cases. The ROC curve is the graphical representation of the pairs (1-specificity, sensitivity). is the area formed by the ROC curve, which equals to 1 in an ideal test. The comparison of the possible combinations for each stage of the study revealed the following results. In the analyses without feature selection, the best results were (1) 100% accuracy and of 0.99 for the combination of PT, LBP and internal masks and (2) 99.5% accuracy and of 0.99 for the combination of GA, LBP and internal masks for the extraction of features. In the analyses with feature selection, the best results were (1) 100% accuracy and of 1.0 for the combination of GA and LBP (feature extraction) with the union of internal and external masks and (2) 98.5% accuracy and of 0.99 for the combination of PT, LBP and the union of internal and external masks.

통합모델의 강수물리과정 모수 최적화를 위한 알고리즘 비교 연구 : 마이크로 유전알고리즘과 하모니 탐색 알고리즘

기상수치예보모델의 강수물리과정은 강수 발생과 연관된 입자의 낙하속도, 부착 및 자동전환, 입자크기분포 등의 과정을 다룬다. 하지만 수치예보모델의 미세물리과정과 모수에는 상당한 불확실성이 내포되어 있다. 수치예보모델의 불확실성을 줄이기 위하여 일반적으로 모수 추정을 사용한다. 이 연구에서는 모수 추정을 위한 최적화 알고리즘으로 마이크로 유전알고리즘과 하모니탐색 알고리즘을 사용하고 우리나라에서 발생한 강수사례에 대해 통합모델의 강수물리과정에서 사용하는 모수를 최적화하였다. 두 알고리즘의 서로 다른 특성으로 인해 최적화 과정 중의 차이가 보였다. 마이크로 유전알고리즘은 440회 수행 후 약 1.033의 적합도로 수렴하였고 하모니탐색 알고리즘은 60번 수행 후 약 1.031의 적합도로 수렴하였다. 이를 통해 하모니탐색 알고리즘이 마이크로 유전알고리즘보다 더 빨리 최적의 모수를 탐색하는 것을 알 수 있었다. 따라서 계산비용이 방대한 기상수치예보모델의 최적화 문제에서 빠른 시간 내에 최적의 모수를 탐색해야 한다면 하모니 탐색 알고리즘이 더 적합하다는 것을 확인하였다. The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

Optimization of the piston bowl geometry and the operating conditions of a gasoline-diesel dual-fuel engine based on a compression ignition engine

This paper describes the optimization of the piston bowl geometry and the operating conditions of a dual-fuel engine based on a compression ignition engine using gasoline port fuel injection and diesel direct injection. KIVA-3V code coupled with a CHEMKIN chemistry solver was used for simulation and a micro-genetic algorithm was used as the optimization algorithm. The micro-genetic algorithm has a smaller population than a conventional genetic algorithm. And in the optimization, the proposed algorithm has six populations for each generation, sixteen variables composed of seven geometry variables and nine operating condition variables. As a result of optimization, a 9% improvement in the gross indicated specific fuel consumption and a simultaneous decrease of the overall NOx and soot emissions were achieved. Also, the amounts of carbon monoxide and unburned hydrocarbons were decreased. The baseline case has a re-entrant shape, while the optimized case has a shallow shape and a narrower spray angle. Furthermore, under operating conditions, the gasoline/total fuel ratio was increased to 90% (similar to a gasoline HCCI (Homogenous charge compression ignition) engine), the EGR (Exhaust gas recirculation) rate was increased to 40% for dilution, and both the boost pressure and initial temperature were decreased.

ELM-RBF Neural Networks using Micro-Genetic Algorithm for Optimization

Thought Extreme Learning Machine-Radial Basis Function(ELM-RBF) can be used easily and can complete learning phase at very fast speed and provide more compact network than classical Extreme Learning Machine(ELM), it still has some room to improvement. Micro-Genetic Algorithms(uGA) improved calculated speed while inherits the Genetic Algorithms advantage of good for optimization and overall search. Considered on these, the paper designed a optimization strategy for ELM-RBF neural network based on uGA. In particular, based on classical RBF-ELM, we use real-u GA algorithm to optimize ELM-RBF hidden layer neurons center and biases value. Experiments results show that ELM-RBF-uGA has better recognition and prediction performance than classical ELM-RBF.

An efficient hybrid algorithm for balancing the load of wireless local area network

Although the IEEE 802.11 wireless local area network (WLAN) provides users of mobile device a convenient method to connect to the Internet, the load of access points (APs) may have a strong impact on the performance of a WLAN, such as the congestion problem. To avoid this issue and to maximize the performance and scalability of a WLAN, an effective method for balancing the load of APs, called a modified multiple-search multi-start framework based micro-genetic algorithm, is presented in this paper. Simulation results show that the proposed algorithm gives a much better solution than the original genetic algorithm does in terms of both the quality and the computation time.

An efficient method for optimizing space steel frames with semi-rigid joints using practical advanced analysis and the micro-genetic algorithm

In this paper, we propose an effective and accurate method for optimizing space steel frames with semi-rigid joints using practical advanced analysis (PAA) and micro-genetic algorithm (μGA). The PAA method using the beam-column approach is applied for capturing the second-order effects and the inelastic behavior of systems, while the zero-length element model is employed for estimating nonlinear behavior of semi-rigid joints. μGA is utilized for finding the global optimal solution, and OpenMP is employed to perform parallel computing in order to efficiently reduce computational time. In this study, unlike many previous researches, not only cross-sectional areas of beam and column members but also semi-rigid connection types are variables of the optimization. The results of some steel frame examples prove that the proposed method is computationally efficient and reliable.

Relevance of Pipe Period on Kelvin-Voigt Viscoelastic Parameters: 1D and 2D Inverse Transient Analysis

AbstractThis paper presents the results of the calibration by means of a microgenetic algorithm, using Kelvin-Voigt viscoelastic parameters, to reproduce experimental unsteady flow tests in a polymeric pipe. During the tests, different pipe lengths—which give rise to different periods of the pressure oscillations—and initial discharges have been considered. The mechanical parameters of the viscoelastic models are estimated using both one-dimensional (1D) and quasi two-dimensional (2D) models. The calibration of Kelvin-Voigt models with 2, 3, 5, and 7 parameters, respectively, proves the substantial independence of the elastic modulus and the dependence of the retardation time on the pipe period (i.e., the pipe length). Moreover, in most cases, the increase in the number of mechanical parameters allows a better simulation of a single transient. However, the larger the number of parameters, the greater the risk of overfitting, and the more difficult the search for general laws of dependence of the parameter...

Integrated modeling system for evaluating water quality benefits of agricultural watershed management practices: Case study in the Midwest

Abstract Nonpoint source pollution (NPS) resulting from agricultural production is one of the major causes of impaired natural resources, adversely affecting the provision of ecosystem services. Best management practices (BMPs) are commonly implemented in watersheds to mitigate agricultural NPS. Appropriate selection of BMP type and its placement in agricultural watersheds are crucial for successful mitigation efforts. In this study, an integrated modeling system was developed to identify scenarios of best management practices such as nutrient management, constructed wetlands and filter strips that provide downstream water quality improvements. The modeling system is a coupling of Soil and Water Assessment Tool (SWAT) for simulating watershed responses and impacts of BMPs, with cost evaluation model solved using Archived Micro-Genetic Algorithm (AMGA2) for generating optimal reduction strategies of NPS pollution at a watershed scale. Its application was demonstrated using tributary watersheds of Lake Decatur – a water supply reservoir located in Illinois, which is listed as impaired for nitrate-nitrogen (N) and total phosphorus (P) in the 2004 Section 303(d) List of Clean Water Act. Study results indicate that nutrient management is the best alternative practice to provide water quality benefits with annual nitrate-N loss reduction of 14.9 percent and cost savings of $6.42 per kg of nitrate-N reduction per hectare (ha). In contrast, constructed wetlands and filter strips were found to incur implementation costs of $10.89/kg N/ha and $1.74/kg N/ha, respectively, including associated land revenue losses. The effectiveness of the filter strips was very limited because of extensive tile drainage in the study watersheds. The integrated modeling system generates valuable information for developing sound watershed management plan and its framework can be adapted to investigate other environmental challenges detrimental to sustainability of water resources.

Design optimization of an angular contact ball bearing for the main shaft of a grinder

A conventional trial and error approach toward the design of non-standard bearings takes a significant amount of time to obtain an adequate design. In this study, a non-standard angular contact ball bearing for the main shaft of a grinder was optimized using design automation and optimization techniques. To manufacture a product as precisely as possible with a grinder, the radial and axial stiffness values of the grinder bearing must be selected as objective functions. To treat two objective functions, this study employed a global criterion method as a multi-objective optimization methodology. Eight constraints on the manufacturing, film thickness, friction, and fatigue life were imposed. Six geometric variables and an axial preload were selected as design variables. All design variables were regarded as discrete because they should have manufacture-possible dimensions. Quasi-static analysis taking dynamic effects into account was employed to analyze bearing performance. For efficient discrete optimization, this study proposed a hybrid method in which a micro-genetic algorithm and regression-based sequential approximate optimizer were both employed. Optimization results revealed that both stiffness values were enhanced while satisfying all design constraints.

Experiment evaluation and simulation optimization of air distribution mode for underwater cabin

The design of air distribution system in a submarine submerged for long periods often faces many constraint conditions. In order to optimize its cabin airflow field, a full-scale experimental bedroom cabin with three three-layer beds was built, and a Flow Visualization with Green Laser (FVGL) method was used to evaluate two types of air distribution mode. Experimental results show that Mode 2 with six individual air vents for lower beds could improve the air quality and heat sensation at lower beds. However, its strong sensation of draft at upper beds could not be avoided. In order to solve this problem, a multi-objective optimal method was built to search the optimal positions of top diffusors. Air age and Predicted Mean Vote (PMV) were chosen as the objective functions. The Micro-Genetic Algorithm (Micro-GA) was used in the presented optimization method to obtain the Pareto-Optimal Front (POF). Finally, the preferred positions of top diffusors can be determined from the POF.

Micro-genetic algorithms for detecting and classifying electric power disturbances

The power quality analysis represents an important aspect in the overall society welfare. The analysis of power disturbances in electrical systems is typically performed in two steps: disturbance detection and disturbance classification. Disturbance detection is usually made through space transform techniques, and their classification is usually performed through artificial intelligence methods. The problem with those approaches is the adequate selection of parameters for these techniques. Due to the advantages of a variant scheme known as the micro-genetic algorithms, in this investigation, a new methodology to directly detect and classify electrical disturbances in one step is developed. The proposed approach is validated through synthetic signals and experimental test on real data, and the obtained results are compared with the particle swarm optimization method in order to show the effectiveness of this methodology.

Strong motion PGA prediction for southwestern China from small earthquake records

Abstract. For regions without enough strong ground motion records, a seismology-based method is adopted to predict motion PGA (peak ground acceleration) values on rock sites with parameters from small earthquake data, recorded by regional broadband digital monitoring networks. Sichuan and Yunnan regions in southwestern China are selected for this case study. Five regional parameters of source spectrum and attenuation are acquired from a joint inversion by the micro-genetic algorithm. PGAs are predicted for earthquakes with moment magnitude (Mw) 5.0, 6.0, and 7.0 respectively and a series of distances. The result is compared with limited regional strong motion data in the corresponding interval Mw ± 0.5. Most of the results ideally pass through the data clusters, except the case of Mw7.0 in the Sichuan region, which shows an obvious slow attenuation due to a lack of observed data from larger earthquakes (Mw ≥ 7.0). For further application, the parameters are adopted in strong motion synthesis at two near-fault stations during the great Wenchuan Earthquake M8.0 in 2008.

Aerodynamic shape optimization of non-straight small wind turbine blades

Small wind turbines usually operate in sub-optimal wind conditions in order to satisfy the demand where it is needed. The aerodynamic performance of small horizontal axis wind turbines highly depends on the geometry. In the present study, the geometry of wind turbine blades are optimized not only in terms of the distribution of the chord and twist angle but also with 3-dimensional stacking line. As the blade with 3-dimensional stacking line is given sweep in the plan of rotation and dihedral in the plan containing the blade and rotor axis, the common used blade element momentum method can no longer provide accurate aerodynamic performance solution. A lifting surface method with free wake model is used as the aerodynamic model in the present work. The annual energy production and the starting performance are selected as optimization objective. The starting performance is evaluated based on blade element method. The optimization of the geometry of the non-straight wind turbine blades is carried out by using a micro-genetic algorithm. Results show that the wind turbine blades with properly designed 3-dimensional stacking line can increase the annual energy production and have a better starting behavior compared with 2-dimensional-optimized blade geometries.

Performance Evaluation of Two Meta-heuristic Schemes in Airfoil Design

This paper presents an optimized design of airfoil cross section using two different meta-heuristic schemes with micro-population. Geometrical coordinates of airfoil section are obtained for achieving the desired target pressure distribution and minimum lift-to-drag ratio. The flow solution for a given angle of attack and free-stream velocity is obtained by using XFOIL and with coupled panel–boundary layer (viscous–inviscid interaction) method. Two optimization schemes namely micro-genetic algorithms and micro-differential evolution with five basic airfoil configurations are employed to achieve required objectives with certain constraints in each case. In minimizing the computation time and effort, a surrogate model based on three-layer perceptron neural network is developed and the results are also reported. The optimized section is analysed finally using computational fluid dynamics solution to know the correctness of the optimized data.

A Parametric Study of Optimal Number and Location of Radiant Heaters in Enclosures with Participating Media

An inverse solution technique is applied to the design of radiant enclosures when design variables are discrete and radiation is the dominant mode of heat transfer.The enclosure contains an absorbing, emitting and linear anisotropic scattering medium in radiative equilibrium. The discrete ordinate method is employed to solve the radiative transfer equation. The goal of the design problem is to find the best number and location of discrete equally power heaters which produce the desired (specified) temperature and heat flux profile over the design surface of enclosures. The inverse problem is formulated as an optimization problem and is solved using a micro-genetic algorithm. Results show that the micro genetic algorithm is able to find the optimal solution by just searching a few percent of feasible solutions.The ability of this methodology is demonstrated by finding the optimal number and location of heaters in an irregular enclosure.Then, the effect of some thermophysical properties, such as extinction coefficient, scattering albedo, scattering phase function, and design surface emissivity, on the optimal solution is considered.

Hybrid optimization implemented for distributed generation parameters in a power system network

Abstract This paper presents a novel hybrid optimization algorithm for optimizing the Distributed Generation (DG) parameters in deregulated power system which improves the stability, reduces the losses and also increases the cost of generation. This Hybrid algorithm which includes Fuzzy-Genetic Algorithm (FGA) is used to optimize the various DG parameters simultaneously. The various parameters taken into consideration are their type, location and size of the DG devices. The simulation was performed on a distribution system and modeled for steady state studies. The optimization results are compared to the solution given by another search method like Genetic Algorithm (GA) and Micro Genetic Algorithm (MGA). The results reveal the benefits of the proposed method, for solving simultaneous combinorial problems of DG devices in a power system network.