Subjects -> STATISTICS (Total: 130 journals)
 Showing 1 - 151 of 151 Journals sorted alphabetically Advances in Complex Systems       (Followers: 11) Advances in Data Analysis and Classification       (Followers: 62) Annals of Applied Statistics       (Followers: 39) Applied Categorical Structures       (Followers: 4) Argumentation et analyse du discours       (Followers: 11) Asian Journal of Mathematics & Statistics       (Followers: 8) AStA Advances in Statistical Analysis       (Followers: 4) Australian & New Zealand Journal of Statistics       (Followers: 13) Bernoulli       (Followers: 9) Biometrical Journal       (Followers: 11) Biometrics       (Followers: 52) British Journal of Mathematical and Statistical Psychology       (Followers: 18) Building Simulation       (Followers: 2) Bulletin of Statistics       (Followers: 4) CHANCE       (Followers: 5) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics - Theory and Methods       (Followers: 11) Computational Statistics       (Followers: 14) Computational Statistics & Data Analysis       (Followers: 37) Current Research in Biostatistics       (Followers: 8) Decisions in Economics and Finance       (Followers: 11) Demographic Research       (Followers: 15) Electronic Journal of Statistics       (Followers: 8) Engineering With Computers       (Followers: 5) Environmental and Ecological Statistics       (Followers: 7) ESAIM: Probability and Statistics       (Followers: 5) Extremes       (Followers: 2) Fuzzy Optimization and Decision Making       (Followers: 9) Geneva Papers on Risk and Insurance - Issues and Practice       (Followers: 13) Handbook of Numerical Analysis       (Followers: 5) Handbook of Statistics       (Followers: 7) IEA World Energy Statistics and Balances -       (Followers: 2) International Journal of Computational Economics and Econometrics       (Followers: 6) International Journal of Quality, Statistics, and Reliability       (Followers: 17) International Journal of Stochastic Analysis       (Followers: 3) International Statistical Review       (Followers: 13) International Trade by Commodity Statistics - Statistiques du commerce international par produit Journal of Algebraic Combinatorics       (Followers: 4) Journal of Applied Statistics       (Followers: 21) Journal of Biopharmaceutical Statistics       (Followers: 21) Journal of Business & Economic Statistics       (Followers: 39, SJR: 3.664, CiteScore: 2) Journal of Combinatorial Optimization       (Followers: 7) Journal of Computational & Graphical Statistics       (Followers: 20) Journal of Econometrics       (Followers: 84) Journal of Educational and Behavioral Statistics       (Followers: 6) Journal of Forecasting       (Followers: 17) Journal of Global Optimization       (Followers: 7) Journal of Interactive Marketing       (Followers: 10) Journal of Mathematics and Statistics       (Followers: 8) Journal of Nonparametric Statistics       (Followers: 6) Journal of Probability and Statistics       (Followers: 10) Journal of Risk and Uncertainty       (Followers: 33) Journal of Statistical and Econometric Methods       (Followers: 5) Journal of Statistical Physics       (Followers: 13) Journal of Statistical Planning and Inference       (Followers: 8) Journal of Statistical Software       (Followers: 21, SJR: 13.802, CiteScore: 16) Journal of the American Statistical Association       (Followers: 72, SJR: 3.746, CiteScore: 2) Journal of the Korean Statistical Society       (Followers: 1) Journal of the Royal Statistical Society Series C (Applied Statistics)       (Followers: 33) Journal of the Royal Statistical Society, Series A (Statistics in Society)       (Followers: 27) Journal of the Royal Statistical Society, Series B (Statistical Methodology)       (Followers: 43) Journal of Theoretical Probability       (Followers: 3) Journal of Time Series Analysis       (Followers: 16) Journal of Urbanism: International Research on Placemaking and Urban Sustainability       (Followers: 30) Law, Probability and Risk       (Followers: 8) Lifetime Data Analysis       (Followers: 7) Mathematical Methods of Statistics       (Followers: 4) Measurement Interdisciplinary Research and Perspectives       (Followers: 1) Metrika       (Followers: 4) Modelling of Mechanical Systems       (Followers: 1) Monte Carlo Methods and Applications       (Followers: 6) Monthly Statistics of International Trade - Statistiques mensuelles du commerce international       (Followers: 2) Multivariate Behavioral Research       (Followers: 5) Optimization Letters       (Followers: 2) Optimization Methods and Software       (Followers: 8) Oxford Bulletin of Economics and Statistics       (Followers: 34) Pharmaceutical Statistics       (Followers: 17) Probability Surveys       (Followers: 4) Queueing Systems       (Followers: 7) Research Synthesis Methods       (Followers: 8) Review of Economics and Statistics       (Followers: 128) Review of Socionetwork Strategies Risk Management       (Followers: 15) Sankhya A       (Followers: 2) Scandinavian Journal of Statistics       (Followers: 9) Sequential Analysis: Design Methods and Applications Significance       (Followers: 7) Sociological Methods & Research       (Followers: 38) SourceOCDE Comptes nationaux et Statistiques retrospectives SourceOCDE Statistiques : Sources et methodes SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques       (Followers: 1) SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance       (Followers: 2) SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques       (Followers: 1) SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques SourceOECD Monthly Statistics of International Trade       (Followers: 1) SourceOECD National Accounts & Historical Statistics SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE       (Followers: 2) SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie SourceOECD Statistics Sources & Methods       (Followers: 1) SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires Stata Journal       (Followers: 9) Statistica Neerlandica       (Followers: 1) Statistical Applications in Genetics and Molecular Biology       (Followers: 5) Statistical Communications in Infectious Diseases Statistical Inference for Stochastic Processes       (Followers: 3) Statistical Methodology       (Followers: 7) Statistical Methods and Applications       (Followers: 6) Statistical Methods in Medical Research       (Followers: 27) Statistical Modelling       (Followers: 19) Statistical Papers       (Followers: 4) Statistical Science       (Followers: 13) Statistics & Probability Letters       (Followers: 13) Statistics & Risk Modeling       (Followers: 3) Statistics and Computing       (Followers: 13) Statistics and Economics       (Followers: 1) Statistics in Medicine       (Followers: 196) Statistics, Politics and Policy       (Followers: 6) Statistics: A Journal of Theoretical and Applied Statistics       (Followers: 14) Stochastic Models       (Followers: 3) Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports       (Followers: 2) Structural and Multidisciplinary Optimization       (Followers: 12) Teaching Statistics       (Followers: 7) Technology Innovations in Statistics Education (TISE)       (Followers: 2) TEST       (Followers: 3) The American Statistician       (Followers: 23) The Annals of Applied Probability       (Followers: 8) The Annals of Probability       (Followers: 10) The Annals of Statistics       (Followers: 34) The Canadian Journal of Statistics / La Revue Canadienne de Statistique       (Followers: 11) Wiley Interdisciplinary Reviews - Computational Statistics       (Followers: 1)
Similar Journals
 Engineering With ComputersJournal Prestige (SJR): 0.485 Citation Impact (citeScore): 2Number of Followers: 5      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1435-5663 - ISSN (Online) 0177-0667 Published by Springer-Verlag  [2652 journals]
• Approximate solution of two-dimensional Sobolev equation using a mixed
Lucas and Fibonacci polynomials
• Abstract: A numerical scheme based on polynomials and finite difference method is developed for numerical solutions of two-dimensional linear and nonlinear Sobolev equations. In this approach, finite difference method is applied for the discretization of time derivative whereas space derivatives are approximated by two-dimensional Lucas polynomials. Applying the procedure and utilizing finite Fibonacci sequence, differentiation matrices are derived. With the help of this technique, the differential equations have been transformed to system of algebraic equations, the solution of which compute unknown coefficients in Lucas polynomials. Substituting the unknowns constants in Lucas series, required solution of targeted equation has been obtained. Performance of the method is verified by studying some test problems and computing E2, E $$_{\infty }$$ and Erms (root mean square) error norms. The obtained accuracy confirms feasibility of the proposed technique.
PubDate: 2021-03-12

• A novel size-dependent nonlocal strain gradient isogeometric model for
functionally graded carbon nanotube-reinforced composite nanoplates
• Abstract: The paper presents a novel nonlocal strain gradient isogeometric model for functionally graded carbon nanotube-reinforced composite (FG-CNTRC) nanoplates. To observe the length scale and size-dependency effects of nanostructures, the nonlocal strain gradient theory (NSGT) is considered. The present model is efficient to capture both nonlocal effects and strain gradient effects in nanoplate structures. In addition, the material properties of the FG-CNTRC are assumed to be graded in the plate thickness direction. Based on the higher order shear deformation theory (HSDT), the weak form of the governing equations of motion of the nanoplates is presented using the principle of virtual work. Afterward, the natural frequency and deflection of the nanoplates are made out of isogeometric analysis (IGA). Thanks to higher order derivatives and continuity of NURBS basic function, IGA is suitable for the weak form of NSGT which requires at least the third-order derivatives in approximate formulations. Effects of nonlocal parameter, strain gradient parameter, carbon nanotube (CNT) volume fraction, distributions of CNTs and length-to-thickness ratios on deflection and natural frequency of the nanoplates are examined and discussed in detail. Numerical results are developed to show the phenomenon of stiffness-softening and stiffness-hardening mechanisms of the present model.
PubDate: 2021-03-12

• Multi-objective efficient global optimization of expensive
simulation-based problem in presence of simulation failures
• Abstract: The multi-objective efficient global optimization (MOEGO), an extension of the single-objective efficient global optimization algorithm with the intention to handle multiple objectives, is one of the most frequently studied surrogate model-based optimization algorithms. However, the evaluation of the infill point obtained in each MOEGO update iteration using simulation tool may fail. Such evaluation failures are critical to the sequential MOEGO method as it leads to a premature halt of the optimization process due to the impossibility of updating the Kriging models approximating objectives. In this paper, a novel strategy to prevent the premature halt of the sequential MOEGO method is proposed. The key point is to introduce an additional Kriging model to predict the success possibility of the simulation at an unvisited point. Multi-objective expected improvement-based criteria incorporating the success possibility of the simulation are proposed. Experiments are performed on a set of six analytic problems, five low-fidelity airfoil shape optimization problems, and a high-fidelity axial flow compressor tandem cascade optimization problem. Results suggest that the proposed MOEGO-Kriging method is the only method that consistently performs well on analytic and practical problems. The methods using the least-square support vector machine (LSSVM) or weighted LSSVM as the predictor of success possibility perform competitively or worse compared with MOEGO-Kriging. The penalty-based method, assigning high objective values to the failed evaluations in minimization problem, yields the worst performance.
PubDate: 2021-03-11

• Variants of bat algorithm for solving partitional clustering problems
• Abstract: Clustering is an exploratory data analysis technique that organize the data objects into clusters with optimal distance efficacy. In this work, a bat algorithm is considered to obtain optimal set of clusters. The bat algorithm is based on the echolocation feature of micro bats. Moreover, some improvements are proposed to overcome the shortcoming associated with bat algorithm like local optima, slow convergence, initial seed points and trade-off between local and global search mechanisms etc. An enhanced cooperative co-evolution method is proposed for addressing the initial seed points selection issue. The local optima issue is handled through neighbourhood search-based mechanism. The trade-off issue among local and global searches of bat algorithm is addressed through a modified elitist strategy. On the basis of aforementioned improvements, three variants (BA-C, BA-CN and BA-CNE) of bat algorithm is developed and efficacy of these variants is tested over twelve benchmark clustering datasets suing intra-cluster distance, accuracy and rand index parameters. Simulation results showed that BA-CNE variant achieves more effective clustering results as compared to BA-C, BA-CN and BA. The simulation results of BA-CNE are also compared with several existing clustering algorithms and two statistical tests are also applied to investigate the statistical difference among BA-CNE and other clustering algorithms. The simulation and statistical results confirmed that BA-CNE is an effective and robust algorithm for handling partitional clustering problems.
PubDate: 2021-03-11

• Adaptive sampling with automatic stopping for feasible region
identification in engineering design
• Abstract: Engineering design is a complex process to find a suitable trade-off among different, and sometimes conflicting, design specifications. In reality, these requirements can be often considered as constraints of the design problem, that can be defined in terms of performance measures or geometrical characteristics of the device under study. In this paper, a new design space exploration methodology is presented for discovering feasible regions in the design space, where the term feasible region indicates the set of all design configurations satisfying all constraints of the design problem. The proposed method is based on Gaussian process metamodels to estimate the feasible region and leverages a information-based adaptive sampling technique to sequentially refine the prediction accuracy, which is applicable for multiple constraints problems. To efficiently stop the adaptive sampling process, a novel framework to estimate the metamodel’s prediction accuracy is proposed. The efficiency, accuracy and robustness of the proposed approach are compared with state-of-art techniques on suitable benchmark problems and practical engineering examples.
PubDate: 2021-03-10

• Creation of small kinetic models for CFD applications: a meta-heuristic
approach
• Abstract: This paper updates a method for generating small, accurate kinetic models for applications in computational fluid dynamics programs. This particular method first uses a time-integrated flux-based algorithm to generate the smallest possible skeletal model based on the detailed kinetic model. Then, it uses a multi-stage optimization process in which multiple runs of a genetic algorithm are used to optimize the rate constant parameters of the retained reactions. This optimization technique provides the user with the flexibility needed to balance the fidelity of the model with their time constraints. The updated method was applied to the reduction of a methane-air model under conditions meant to approximate the end of a compression stroke of an internal combustion engine. When compared to previous techniques, the results showed that this method could produce a more accurate model in considerably less time. The best model obtained in this study resulted in relative errors ranging from 0.22 to 1.14% on all six optimization targets. This reduced model was also able to adequately predict optimization targets for certain operating conditions, which were not included in the optimization process.
PubDate: 2021-03-10

• Fast numerical approximation for the space-fractional semilinear parabolic
equations on surfaces
• Abstract: In this work, a fast numerical method is considered to solve the space-fractional semilinear parabolic equations on closed surfaces. It is a challenge in that how to define the space fractional operator and corresponding semilinear parabolic equation with the energy functional defined on surface. To overcome it, using the local tangential space, we construct the spectral approximation for the space fractional operator on surfaces and apply the matrix transfer technique to avoid the difficulty of fractional nonlocality. The main advantage of the matrix transfer method is the completed diagonal representation of fractional operators from eigenvalue decomposition. Moreover, the time-discrete error estimates are presented as well as the energy stability. Various numerical examples are carried out to verify the theoretical results.
PubDate: 2021-03-10

• Finite element model updating of a multispan bridge with a hybrid
metaheuristic search algorithm using experimental data from wireless
triaxial sensors
• Abstract: The Guadalquivir bridge is a large-scale twin steel truss bridge located in Spain that opened to traffic in 1929. Since the bridge has come into operation for a long time, structural health monitoring (SHM) is strictly necessary to guarantee safety and avoid serious incidents. This paper proposes a novel approach to model updating for the Guadalquivir bridge based on the vibration measurements combined with a hybrid metaheuristic search algorithm. Cuckoo Search (CS) is an evolutionary algorithm derived from global search techniques to look for the best solution. Nevertheless, CS contains some fundamental defects that may reduce its effectiveness in dealing with optimization issues. A main drawback of CS arises in the low convergence level because CS applies fixed values for parameters when looking for the optimal solution. In addition, CS relies a lot on the quality of original populations and does not have the capability to enhance the quality of the next generations. If the position of the original particles is far from the optimal places, it may be challenging to look for the best solution. To remedy the shortcomings of CS, we propose a hybrid metaheuristic algorithm (HGAICS) employing the advantages of both Genetic Algorithm (GA) and Improved Cuckoo Search (ICS) to solve optimization problems. HGAICS contains two outstanding characteristics as follows: (1) GA is employed to create original particles with the best quality based on the capacity of crossover and mutation operators and (2) those particles are then applied to look for the global best derived from the flexible and global search ability of ICS. This paper also presents the application of wireless triaxial sensors (WTSs) taking the place of classical wired systems (CWSs) to the measurements. The use of WTSs increases dramatically the freedom in setting up experimental measurements. The results show that the performance of the proposed hybrid algorithm not only determines uncertain parameters of the Guadalquivir bridge properly, but also is more accurate than GA, CS, and improved CS (ICS). A MATLAB package of the proposed method (HGAICS) is available via GitHub: https://github.com/HoatranCH/HGAICS.
PubDate: 2021-03-09

• A multi-objective optimization algorithm for feature selection problems
• Abstract: Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.
PubDate: 2021-03-09

• Stability evaluation of dump slope using artificial neural network and
multiple regression
• Abstract: The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage.
PubDate: 2021-03-09

• Postbuckling of multilayer cylindrical and spherical shell panels
reinforced with graphene platelet by isogeometric analysis
• Abstract: The present work fills a gap on the postbuckling behavior of multilayer functionally graded graphene platelet reinforced composite (FG-GPLRC) cylindrical and spherical shell panels resting on elastic foundations subjected to central pinching forces and pressure loadings. Based on a higher-order shear deformation theory and the von Kármán’s nonlinear strain–displacement relations, the governing equations of the FG-GPLRC cylindrical and spherical shell panels are established by the principle of virtual work. The non-uniform rational B-spline (NURBS) based isogeometric analysis (IGA), the modified arc-length method and the Newton’s iteration method are employed synthetically to obtain nonlinear load–deflection curves for the panels numerically. Several comparative examples are performed to test reliability and accuracy of IGA and arc-length method in present formulation and programming implementation. Parametric investigations are carried out to illustrate the effects of dispersion type of the graphene platelet (GPL), weight fraction of the GPL, thickness of the panel, radius of the panel and parameters of elastic foundation on the load–deflection curves of the FG-GPLRC shell panels. Some complex load–deflection curves of the FG-GPLRC cylindrical and spherical shell panels resting on elastic foundations may be useful for future references.
PubDate: 2021-03-09

• Fluid–structure–soil interaction effects on the free vibrations of
• Abstract: This study investigates the effects of fluid–structure and soil–structure interaction on the free vibration response of functionally graded sandwich plates. To this aim, an exemplary problem is analyzed, whereas a metal/ceramic sandwich plate is placed at the bottom of a tank filled in with fluid. Two cases are considered: (i) soft core, i.e., a sandwich plate with metal core and ceramic skins, and (ii) hard core, i.e., a sandwich plate with ceramic core and metal skins. In both cases, the skins are modelled as suitable functionally graded materials (FGMs). The soil is modelled as a Pasternak foundation. The free vibration analysis is carried out according to the extended higher order sandwich plate theory (EHSAPT). The fluid is assumed to be inviscid, incompressible, and irrotational. Hamilton’s principle is exploited to deduce the governing equations and the corresponding boundary conditions. The Rayleigh–Ritz method with two-variable orthogonal polynomials is used to compute the natural frequencies of the sandwich plate. The adopted approach is first validated through comparison with results published in the literature. Then, the effects are studied of several parameters on the dynamic response of the system.
PubDate: 2021-03-09

• Development of an RVE using a DEM–FEM scheme under modified approximate
periodic boundary condition to estimate the elastic mechanical properties
of open foams
• Abstract: In this article, a methodology based on Discrete Element Method (DEM) and Finite Elements Method (FEM) combined with modified Approximate Periodic Boundary Condition (mAPBC) has been utilized to develop a cubic Representative Element Volume (RVE). The developed methodology has then been effectively utilized to estimate the elastic mechanical properties of open foams, validated for the case of Ti open-cell foams with non-regular distribution of spherical pores. The non-regular distribution of overlapping pores was generated by means of DEM, while the homogenized elastic constants for foams with 30–70 percentage porosity are computed using FEM based on a Small Lineal Perturbation Method (SLPM). The accuracy of the developed approach has been demonstrated through the comparison of the simulations obtained for different RVE sizes with experimental results and predictive models available in literature. Graphic abstract
PubDate: 2021-03-08

• Improving the performance of LSSVM model in predicting the safety factor
for circular failure slope through optimization algorithms
• Abstract: Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.
PubDate: 2021-03-08

• Towards novel deep neuroevolution models: chaotic levy grasshopper
optimization for short-term wind speed forecasting
• Abstract: High accurate wind speed forecasting plays an important role in ensuring the sustainability of wind power utilization. Although deep neural networks (DNNs) have been recently applied to wind time-series datasets, their maximum performance largely leans on their designed architecture. By the current state-of-the-art DNNs, their architectures are mainly configured in manual way, which is a time-consuming task. Thus, it is difficult and frustrating for regular users who do not have comprehensive experience in DNNs to design their optimal architectures to forecast problems of interest. This paper proposes a novel framework to optimize the hyperparameters and architecture of DNNs used for wind speed forecasting. Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters. For designing the enhanced version of GOA, the chaotic theory and levy flight strategies are applied to make an efficient balance between the exploitation and exploration phases of the GOA. Moreover, the mutual information (MI) feature selection algorithm is utilized to select more correlated and effective historical wind speed time series features. The proposed model’s performance is comprehensively evaluated on two datasets gathered from the wind stations located in the United States (US) for two forecasting horizons of the next 30-min and 1-h ahead. The experimental results reveal that the proposed model achieves the best forecasting performance compared to seven prominent classical and state-of-the-art forecasting algorithms.
PubDate: 2021-03-08

• An improved Kriging-based approach for system reliability analysis with
multiple failure modes
• Abstract: Reliability analysis with multiple failure modes is needed because more than one failure mode exists in many engineering applications. Kriging-based surrogate model is widely adopted for component reliability analysis because of its high computational efficiency. Compared with Kriging-based component reliability analysis, selecting the sample points that affect the system performance is more difficult than that of a single failure mode in system reliability analysis. Therefore, how to select suitable sample points is a key problem in system reliability analysis. Meanwhile, reducing the number of calls to the performance functions is challenging, especially for systems with time-consuming performance functions. In this paper, an improved Kriging-based system reliability analysis approach is proposed based on the two strategies. In strategy 1, the initial sample points are determined by considering only two different cases: (a) the candidate samples are selected from the safe regions only for series systems; (b) the candidate samples are selected from the failure regions only for parallel systems. Therefore, samples having little contributions to the composite performance function are avoided. In strategy 2, the sample points determined in strategy 1 will be further optimized by interpolating. From comparisons with three reported methods in numerical examples, the efficiency and accuracy of the proposed method are illustrated.
PubDate: 2021-03-08

• Optimization to optimization (OtoO): optimize monarchy butterfly method
with stochastics multi-parameter divergence method for benchmark functions
• Abstract: Optimization to optimization (OtoO) approach is proposed in this study. It aims to increase an optimization algorithm performance. OtoO approach has two types of optimization methods. First is essential algorithm, which is used for solution of the basic problem. Second is auxiliary algorithm that adjusted the parameters of the essential algorithm. In this study, the monarchy butterfly optimization (MBO) method and stochastic multi-parameter divergence optimization (SMDO) method were defined as essential algorithm and auxiliary algorithm, respectively. Constant parameters of the MBO method that affect performance (Keep, Max. Step Size, period and BAR) are primarily optimized on benchmark functions with the SMDO algorithm, and results are compared with each other and classical MBO, ABC (Artificial Bee Colony), ACO (Ant Colony), BBO (Biogeography-based), SGA (Simple Genetic) and DE (Differential Evolution) algorithms. In addition, OtoO approach is also tried via composite benchmark functions. In addition, PI and PID controllers were designed for the load frequency control of a hybrid power system. Results are compared with the FA (Firefly Algorithm) and GA (Genetic Algorithm) results. Results demonstrate that the performance of algorithms can be increased without disrupting the basic philosophy of algorithms and hybridizing algorithms with the proposed OtoO approach via benchmark functions and engineering problems.
PubDate: 2021-03-07

• Nonlinear analysis of size-dependent frequencies in porous FG curved
nanotubes based on nonlocal strain gradient theory
• Abstract: A nonlocal strain gradient model is developed in this research to analyse the nonlinear frequencies of functionally graded porous curved nanotubes. It is assumed that the curved nanotube is in contact with a two-parameter nonlinear elastic foundation and is also subjected to the uniform temperature rise. The non-classical theory presented for curved nanotubes contains a nonlocal parameter and a material length scale parameter which can capture the size effect. A power law distribution function is used to describe the graded properties through the thickness direction of curved nanotubes. The even dispersion pattern is used to model the porosities distribution. The high-order shear deformation theory and the von Kármán type of geometric non-linearity are utilized to obtain the nonlinear governing equations of the structure. The size-dependent equations of motion for the large amplitude vibrations of curved nanotubes are obtained by employing Hamilton’s principle. The analytical solutions are extracted for the curved nanotube with immovable hinged-hinged boundary conditions. Size-dependent frequencies of the curved nanotube exposed to thermal field are obtained using the two-step perturbation technique and Galerkin procedure. The effects of important parameters such as nonlocal and length scale parameters, temperature field, elastic foundation, porosity, power law index and geometrical parameters are studied in detail.
PubDate: 2021-03-07

• A novel boosting ensemble committee-based model for local scour depth
around non-uniformly spaced pile groups
• Abstract: Prediction of the scour depth around non-uniformly spaced pile groups (PGs) is one of the most complex problems is hydraulic engineering. Different types of empirical methods have been developed for estimating the scour depth around the PGs. However, the majority of the existing methods are based on simple regression methods and have serious limitations in modelling the highly nonlinear and complex relationships between the scour depth and its influential variables, especially for the non-uniformly spaced pile. Hence, this study combines prediction powers of tree popular machine learning (ML) methods, namely, Gaussian process regression (GPR), random forest (RF), and M5 model tree (M5Tree) using novel Least Least-squares (LS) Boosting Ensemble committee-based data intelligent technique to more accurately estimate local scour depth around non-uniformly spaced pile groups. A total of 353 laboratory experiments data were compiled from published papers. non-dimensional results obtained demonstrated that the ensemble model can more accurately estimate the scour depth than the individual predictions of the GPR, RF, and M5Tree models. The proposed Ensemble model with correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage of error (MAPE) of 0.972, 0.0153 m, and 10.89%, respectively, significantly outperformed all existing empirical models. Furthermore, the sensitivity analysis demonstrated that the pile diameter is the most influential variable in estimating the scour depth.
PubDate: 2021-03-06

• Effect of parametric enhancements on naked mole-rat algorithm for global
optimization
• Abstract: Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating patterns of NMRs present in nature. The algorithm though is very simple and linear in nature but suffers from poor exploration during the initial stages and poor exploitation towards the end. Thus to overcome these problems and estimate the effect of basic parameters of NMRA, six new inertia weight strategies and five new mutation operators have been employed. After careful investigation, a new Lévy mutated NMRA (LNMRA) is proposed. The new algorithm employs combined properties of inertia weights and mutation operators altogether. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes and testing is done on CEC 2005, CEC 2014 benchmark problems and real world optimization problem of dual band-notched ultra-wideband (UWB) antenna design. Experimental and statistical results show that the proposed LNMRA is better with respect to other algorithms under comparison.
PubDate: 2021-03-05

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