Real-valued Genetic Algorithm Research Articles

Modelling a Response of Complex-Phase Steel at High Strain Rates.

In this article, a response of the complex-phase high-strength steel SZBS800 was modelled by considering the strain-rate influence. The material's response was first measured with a series of standard tensile tests at lower strain rates. Higher strain rates were achieved using the unconventional test of shooting the ball into flat specimens. A viscoplastic formulation of the Cowper-Symonds material model was applied to consider the strain-rate effects. The parameters SIGY, p, and C of the material model were estimated using a step-wise procedure. First, rough estimates of the three parameters were obtained from the tensile tests using the grid search method. Then, the parameters p and C were fine-tuned using the reverse engineering approach. With the help of explicit dynamic simulations and all the experimental data, a multi-criteria cost function was defined and applied to obtain a smooth response function for the parameters p and C. Its optimum was determined by a real-valued genetic algorithm. The optimal values of the estimated parameters model the material response well, although a domain of optimum candidates spans two orders of magnitude for the parameter p and a few orders of magnitude for the parameter C.

Experimental investigation on temperature-dependent effective thermal conductivity of ceramic fiber felt

This paper presents a method to invert the effective thermal conductivity of ceramic fiber felt under high temperatures using experimentally measured specific heat capacity, density, and transient temperature data. The method combines the solution of the heat transfer equation with an inversion method based on real-valued genetic algorithm (GA) optimization, and 11 target parameters can be simultaneously inverted with one experimental dataset. The effective thermal conductivities of ceramic fiber felt were obtained by inversion under ambient pressures of 2 Pa and 100 kPa, compression ratios of 0.8–1.0, and temperature ranges of 295∼1273 K. The results indicates that the effective thermal conductivities of ceramic fiber felt derived from the inversion method are in concurrence with the outcomes of the steady state method. The effective thermal conductivities of the ceramic fiber felt range from 0.069 to 0.321 W m−1 K−1. It is observed that the effective thermal conductivity increases with higher temperature, and decreases with growing compression ratio. The impact of ambient pressure on effective thermal conductivity is linked to the level of compression.

Applied real-valued genetic algorithm for an extended model of economic lot, purchase and delivery scheduling problem

Abstract Supply chain management intends to integrate supply chains' activities such as material flow, information flow and financial issues. Material flow management is the most significant issue since the inventory level in the whole supply chain could be optimized by an integrated plan. In other words, when one member of the supply chain plans to reduce its inventory level solely, despite reducing inventory in this node the inventory will be stocked in other partners' warehouses. Therefore, in this paper a new mathematical model has been developed to facilitate the process of finding the optimum solution in economic production, purchase and delivery lots and their schedules in a three-echelon supply chain environment; including raw material in suppliers, manufacturer and assembly facility as a customer. The manufacturer with a flow shop system provides its requirements from supplier, assemble multiple products, and delivers products to the customer (automotive OEM alike) on an optimum multiple delivery points. The delivery cycles would be identified through the production common cycle regarding the supply chain flexibility. Finally, a modified real-valued Genetic Algorithm (MRGA), and an Optimal Enumeration Method (OEM) are developed, and some numerical experiments have been done and compared as well.

A flexible rolling regression framework for the identification of time-varying SIRD models

The present paper introduces a rolling regression approach to fitting the time-varying parameters of a susceptible-infected-recovered-deceased (SIRD) model. Our approach provides a domain-aware optimization environment applicable to any epidemiology compartmental model that is linear with respect to those parameters. Our framework is presented as a mixed integer bilevel nonlinear programming problem that is tackled with a real-valued genetic algorithm where we optimize the window size used for regression and estimate the hidden population of infected, recovered, and dead individuals that is not captured by the data available. We test our optimization framework with 2020 COVID-19 data from the state of Minnesota and compare our results both quantitatively and qualitatively, concluding that they are consistent with previously published research.Finally, we compare our proposed method against one of our benchmarks and show that the flexibility of our approach offers better system identification, thus highlighting the importance of domain-aware models that can infer the size of hidden populations from the recorded data and with the aid of equations that describe efficiently the dynamics of an epidemic.

Global Simultaneous Optimization of Oil, Hysteretic and Inertial Dampers Using Real-Valued Genetic Algorithm and Local Search

A method for global simultaneous optimization of oil, hysteretic and inertial dampers is proposed for building structures using a real-valued genetic algorithm and local search. Oil dampers has the property that they can reduce both displacement and acceleration without significant change of natural frequencies and hysteretic dampers possess the characteristic that they can absorb energy efficiently and reduce displacement effectively in compensation for the increase of acceleration. On the other hand, inertial dampers can change (prolong) the natural periods with negative stiffness and reduce the effective input and the maximum acceleration in compensation for the increase of deformation. By using the proposed simultaneous optimization method, structural designers can select the best choice of these three dampers from the viewpoints of cost and performance indices (displacement, acceleration). For attaining the global optimal solution which cannot be attained by the conventional sensitivity-based approach, a method including a real-valued genetic algorithm and local search is devised. In the first stage, a real-valued genetic algorithm is used for searching an approximate global optimal solution. Then a local search procedure is activated for enhancing the optimal character of the solutions by reducing the total quantity of three types of dampers. It is demonstrated that a better design from the viewpoint of global optimality can be obtained by the proposed method and the preference of damper selection strongly depends on the design target (displacement, acceleration). Finally, a multi-objective optimization for the minimum deformation and acceleration is investigated.

Enhance the Quality of Collaborative Filtering Using Tagging

Background: Due to enormous data on web sites, recommending users for every item is impossible. For this problem, Recommender Systems (RS) are introduced. RS is categorized into Content-Based (CB), Collaborative Filtering (CF) and Hybrid RS. Based on these techniques, recommendations are made to the user. In this, CF is the recent technique used in RS in which tagging features also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues, here we have proposed Tag and Time weight model with GA in Collaborative Tagging. Methods: Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of missing value prediction. Here in this system, the sparsity problem can be removed using missing value prediction and cold start problems are removed using tag and time weight model using GA. Results: In this study, we have compared the results of Collaborative Filtering with Cosine Similarity (CF-CS), Collaborative Filtering with Diffusion Similarity (CF-DS), Tag and Time Weight Model with Diffusion Similarity (TAW-TIW-DS) and Tag and Time Weight Model Using Diffusion Similarity and Genetic Algorithm (TAW-TIW-DS-GA). Conclusion: Here we have compared the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Hit-rate and Hit-rank. Based on these metrics for every split, TAW-TIW-DS-GA showed best results as compared to the existing approach.

The time-dependent diffusion equation: An inverse diffusivity problem

We find a solution of an unknown time-dependent diffusivity a(t) in a linear inverse parabolic problem by a modified genetic algorithm. At first, it is shown that under certain conditions of data, there exists at least one solution for unknown a(t) in (a(t), T (x, t)), which is a solution to the corresponding problem. Then, an optimal estimation for unknown a(t) is found by applying the least-squares method and a modified genetic algo rithm. Results show that an excellent estimation can be obtained by the implementation of a modified real-valued genetic algorithm within an Intel Pentium (R) dual-core CPU with a clock speed of 2.4 GHz.

A Generalized Encoding System for Alpha Oscillations Through Visual Saliency Analysis.

By learning how the brain reacts to external visual stimuli and examining possible triggered brain statuses, we conduct a systematic study on an encoding problem that estimates ongoing EEG dynamics from visual information. A novel generalized system is proposed to encode the alpha oscillations modulated during video viewing by employing the visual saliency involved in the presented natural video stimuli. Focusing on the parietal and occipital lobes, the encoding effects at different alpha frequency bins and brain locations are examined by a real-valued genetic algorithm (GA), and possible links between alpha features and saliency patterns are constructed. The robustness and reliability of the proposed system are demonstrated in a 10-fold cross-validation. The results show that stimuli with different saliency levels can induce significant changes in occipito-parietal alpha oscillations and that alpha at higher frequency bins responded the most in involuntary attention related to bottom-up-based visual processing. This study provides a novel approach to understand the processing of involuntary attention in the brain dynamics and would further be beneficial to the development of brain-computer interfaces and visual design.

High-cycle fatigue and fracture behaviours of SLM AlSi10Mg alloy

Abstract The high-cycle fatigue and fracture behaviours of the selective laser melting (SLM) AlSi10Mg alloy were investigated. Flat specimens were designed directly in the shape required for the fatigue tests under pulsating loading in tension (R=0, R is the dynamic factor). The fatigue–life (S–N) curves were modelled with a conditional Weibull's probability density function, where the real-valued genetic algorithm (GA) and the differential ant-stigmergy algorithm (DASA) were applied to estimating the needed Weibull's parameters. The fractography of the fatigue specimens showed that the fatigue cracks initiated around the surface defects produced by SLM and then propagated in an unstable manner. However, the presence of large SLM defects mainly influenced the crack initiation period and did not have a strong influence on the crack propagation. The obtained experimental results present a basis for further investigation of the fatigue behaviour of advanced materials and structures (e.g. cellular metamaterials) fabricated by additive manufacturing (AM). Especially, in the case of two-dimensional cellular structures, the cross-section of cellular struts is usually rectangular which corresponds to the specimen shape considered in this work.

A Real-Valued Genetic Algorithm for Optimization of Sensor Placement for Guided Wave-Based Structural Health Monitoring

The paper presents a novel implementation of the genetic algorithm (GA) to improve the coverage of the sensor network for damage detection using guided waves. The implementation allows depiction of sensor locations with real values which is closer to the real-life situation. Also, additional features such as proximity checks and node insertions have been implemented in order to improve the convergence of the GA as well as the thoroughness of the search space. For the traditional integer-based implementation, the size of the problem is large but finite. For the real-valued implementation, the problem size can indeed be infinitely large. So added measures have been introduced such as a two-step optimization process for the reduction in size and improved convergence.

Indirect estimation of interregional freight flows with a real-valued genetic algorithm

This paper introduces a method for estimating the interregional transportation of certain commodities in those cases where the commodity flows are not readily available and only aggregated flows per origin-destination pair are provided. We use a doubly-constrained gravity model to find a matrix of aggregated flows that is as similar as possible to the available data, in the sense of the standardized root mean square error. This model is calibrated via a real-valued genetic algorithm that uses a combination of global and local searches to find a set of optimal parameters of the deterrence function under study in the gravity model. This method is introduced as an application to estimating the disaggregated flows of ten different products among the fifteen regions of peninsular Spain between 2007 and 2016. After testing several formulations, we conclude that an exponential deterrence function calibrated with data from 2010 is as effective to estimate the flows in this 10-year span as other more complex options, which emphasizes the time transferability of our model.

Innovation design and optimization management of a new drive system for plug-in hybrid electric vehicles

A multi-mode coupling drive system has been designed and controlled to improve the dynamic characteristics and fuel economy of plug-in hybrid electric vehicles, which also can make full use of the configured superiority of centralized drive systems and distributed drive systems and avoid their structural defects. The configuration evolution process, working mechanism and drive modes of the multi-mode coupling drive system are introduced. The powertrain model is established for the target vehicle. Based on Charge Depleting-Charge Sustaining energy management strategy, an Electric Vehicle-Charge Sustaining energy management strategy is developed. The Improved Real-valued Genetic Algorithm is used to optimize the system structural and control parameters, it can help prioritize the drive modes which are based on the proposed energy management strategy. While ensuring the vehicle dynamics, the best energy allocation is achieved. The results show that comparing with a series distributed drive hybrid system and the intelligent Multi-Mode Drive (i-MMD) hybrid system under the NEDC condition, the 100-km fuel consumption of the optimized multi-mode coupling drive system is reduced by 16.52% and 15.40%. Respectively, it further proves the superiority of the drive system in improving vehicle economy.

MIMO antenna selection using biogeography‐based optimization with nonlinear migration models

SummaryThis papers deals with the problem of antenna selection (AS) for a multiple‐input multiple‐output (MIMO) wireless system under the constraint of the channel capacity maximization. The biogeography‐based optimization (BBO) algorithm is applied on the joint transmitter and receiver AS problem. Moreover, the performance of different BBO migration models is compared with a real valued genetic algorithm (RVGA) as well as with the ant colony optimization (ACO). Representative simulation scenarios are provided in detail, involving selection of 2 × 4,3 × 5,4 × 6,8 × 8 antennas in a 16 × 16 MIMO system. The numerical results demonstrate the efficiency and the applicability of the BBO algorithm in modern MIMO wireless systems.

Frequency-based damage detection in cantilever beam using vision-based monitoring system with motion magnification technique

This article proposes a method for damage detection using vision-based monitoring with motion magnification technique. The methods based on the vibration characteristics of structures such as natural frequency, mode shapes, and modal damping have been applied to structural damage detection. However, the conventional methods have limitations for practical applications. Vision-based monitoring system can be employed as a new structural monitoring system because of its simplicity, potentially low cost, and unique capability of collecting high-resolution data. A methodology called video motion magnification has been developed to amplify non-visible small motions in a video to reveal the dynamic response. The video motion magnification method can be applied to measure small displacements to calculate the natural frequencies and the operational deflection shapes of the structures. Unlike conventional optimization methods, a genetic algorithm explores the entire solution space and can obtain the global optimum. In this article, identification of the location and magnitude of damage in a cantilever beam is formulated as an optimization problem using a real-value genetic algorithm by minimizing the objective function, which directly compares the first three natural frequencies changes from the phase-based motion magnification measurement and from the analytical model of a damaged cantilever beam.

A hierarchical soft computing model for parameter estimation of curve fitting problems

The aim of this paper is to present an alternative solution model to estimate the coefficients of large-scaled linear and nonlinear real-life problems due to the fact that least squares and least median squares parameter estimators have some drawbacks when including so many input variables or increased size of the real-world problems. The study presents a hierarchical soft computing model (SOFTC) that consists of three stages. The first stage constitutes a real-valued breeder genetic algorithm (RVBGA). The second stage is constructing a simulated annealing (SA) algorithm in which the best parameter estimation of the RVBGA is selected as its initial point. The third stage is developing a hierarchical soft computing model by using fuzzy recombination method. SOFTC optimizes the best parameter estimations of this algorithms and it provides a trust region for parameter estimation. Three test problems, one of which is linear and others are nonlinear, are used to examine robustness of proposed models. SOFTC, RVBGA_SA and RVBGA algorithms performed the best parameter estimations, respectively, for the three test problems. The results which are discussed in detail are promising for future usage of these algorithms.

Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems

Tolerance-rough-set-based classifiers (TRSCs) are known to operate effectively on real-valued attributes for classification problems. This involves creating a tolerance relation that is defined by a distance function to estimate proximity between any pair of patterns. To improve the classification performance of the TRSC, distance may not be an appropriate means of estimating similarity. As certain relations hold among the patterns, it is interesting to consider similarity from the perspective of these relations. Thus, this study uses grey relational analysis to identify direct influences by generating a total influence matrix to verify the interdependence among patterns. In particular, to maintain the balance between a direct and a total influence matrix, an aggregated influence matrix is proposed to form the basis for the proposed grey-total-influence-based tolerance rough set (GTI-TRS) for pattern classification. A real-valued genetic algorithm is designed to generate the grey tolerance class of a pattern to yield high classification accuracy. The results of experiments showed that the classification accuracy obtained by the proposed method was comparable to those obtained by other rough-set-based methods.

Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents

The higher heating values (HHVs) of 11 non-wood lignocellulosic materials from Turkey were measured experimentally and calculated incorporating various theoretical models with the values of both lignin and extractive contents. Multiple linear regression (MLR) and real valued genetic algorithm (RVGA) were used to derive the theoretical models. A non-linear RVGA6 model was determined as the best non-linear model considering the experimental results with a regression coefficient of 92% coefficient of determination (R2), 0.301 sum of squared errors (SSE), 0.301 mean squared errors (MSE), 0.548 root mean squared errors (RMSE) and 0.0187 mean absolute percentage error (MAPE) and is proposed as a better alternative for theoretical HHV calculations to the multiple linear modellings such as MLR and RVGA1.

An Effective Software Reliability Estimation with Real-Valued Genetic Algorithm

The implemented approach is powerful method for estimating reliability of the software parameters growing SRGM by utilizing an Algorithm which is known as RGA. The full form of RGA is Real-valued Genetic Algorithm. Parameters required for current SRGM, if we take an illustration, the Failures average number or identification rate of the failure utilizing the techniques which are numerical, estimation of the maximum probability or estimation of minimum square.RGA means the free form of SRGM parameter estimation limitations. Instead of these, this can be much adapted for optimizing domain continuously compared to the algorithm of the binary genetic. The operators of GA which is 2 real valued crossovers& mutation of non-uniform interfaced for enhancing SRGM parameters estimation execution and accuracy enhancement. I led tests over eight datasets which are real valued to contrast implemented scheme & techniques of the numerical & another generic algorithm which are typical. The results describes that in estimation of SRGM parameters, the RGA is the most powerful compared to the others. So that we can trust the RGA which is the right solution for getting the efficient software quality with estimation of reliable accuracy.

Robust power-law kinetic model for the autothermal reforming of glycerol over metal oxide catalysts

The kinetics of autothermal reforming of glycerol in a tubular fixed-bed reactor using a nickel-based catalyst supported over Ce0.5Zr0.33Gd0.16O2−δ has been investigated for the first time for hydrogen production. The study was conducted using steam-to-glycerol ratio (6, 9, 12), oxygen-to-glycerol ratios (0.2, 0.5, 0.8) and weight of catalyst/glycerol molar ratio (W/FG0) of 5.05, 6.55 and 8.06 kgcatalyst h kmol G −1 at atmospheric pressure and reaction temperature in the range (723–873 K). An integral analysis combined with a real-valued genetic algorithm optimizer was used to estimate rate model parameters. This analysis resulted in a power law rate model with an apparent activation energy of 53.3 kJ mol−1 and reaction orders 0.16, 0.74 and − 0.49 with respect to glycerol, oxygen and steam partial pressures. The model predicted conversions and the experimentally obtained conversions exhibited close resemblance with an average absolute deviation of 7%.

Experimental investigations on temperature-dependent effective thermal conductivity of nanoporous silica aerogel composite

This paper presents an inverse method for retrieving temperature-dependent effective thermal conductivity of nanoporous silica aerogel composite at temperature ranging from 280K to 1080K and gas pressure between 0.01Pa and 100kPa from experimentally measured transient temperature data. This was achieved by combining a forward method solving combined conductive and radiative heat transfer accounting for temperature-dependent thermal conductivity, and an inverse method based on a real-valued genetic algorithm (GA) optimization. First, the sensitivity coefficients for the transient temperature profiles with respect to the variation of effective thermal conductivity were investigated. Then, several numerical experiments, in which the “experimental data” was numerically generated, were performed to illustrate the robustness and accuracy of the inverse method for retrieving the temperature-dependent effective thermal conductivity from transient temperature history. The experimental data were used to retrieve the effective thermal conductivities of silica aerogel composite, the results fell within 0.014–0.044W·m−1·K−1 for the temperature between 280 and 1080K and gas pressure from 0.01Pa to 100kPa, and showed nonlinear increasing trend with increasing temperature, and with increasing gas pressure.