Fitness Function For Optimization Research Articles

Landslide susceptibility analysis based on a PSO-DBN prediction model in an earthquake-stricken area

In recent years, the major geological hazard of landslides has greatly impact normal human life. Deep belief networks (DBN) is a commonly used deep learning model, and the DBN hyperparameter determination problem is the key to its application. To improve the accuracy of regional landslide susceptibility prediction, this paper introduces the particle swarm algorithm (PSO) to determine the hyperparameters of the DBN; this is applied to regional landslide susceptibility prediction. Firstly, PSO is used to optimize the hyperparameters of the DBN and obtain a set of hyperparameters with the optimal fitness function. A landslide susceptibility prediction model based on PSO-DBN is then constructed and the K-fold cross-validation method is used to determine its accuracy. The model is applied to landslide susceptibility prediction in the most impacted area of the Wenchuan earthquake to analyze the model’s accuracy. Finally, model susceptibility analysis is performed. The research results show that the final optimal model accuracy of the PSO-DBN model is 95.52%, which is approximately 28.31% and 15.35% higher than that of the logistic regression (LR) model and the common DBN model, respectively. The Kappa coefficient is 0.883, which is higher than that of the LR model. Compared with the LR model and the common DBN model, Kappa coefficient is improved by approximately 0.542 and 0.269 respectively; the area under the curve (AUC) is 0.951, which is improved by approximately 0.201 and 0.080 compared to the LR model and the common DBN model. The susceptibility of the model to the inertia factor is low, the average change in model accuracy (when the inertia factor changes by 0.1) is approximately 0.1%, and the overall stability of the model is high. The landslide susceptibility level is very high. The area includes 219 landslide points, which account for 39.2% of total landslide points. In the area with a high level of landslide susceptibility are 191 landslide points, accounting for 34.2% of total landslide points. Together, the two contain approximately 73.4% of the landslide points. This indicates that the model prediction results agree well with the spatial distribution characteristics of the landslide.

An equilibrium model of the electricity market considering the participation of virtual power plants

Led by the carbon peak and neutrality targets, there is a consensus for renewable energy to compete in the electricity market alongside conventional energy sources. Based on co-evolutionary algorithms, this paper proposes an equilibrium model of electricity market with the participation of virtual power plants composed of wind farms and distributed energy storage. The model constructs a secondary clearing mechanism with the aim of promoting hydropower consumption and simulates the game behavior of various players in a market environment from the perspective of power producers. A co-evolutionary algorithm is used to solve the market equilibrium point for the game model, and the optimal fitness function is used to judge whether the generator’s strategy combination is optimal. The simulation results validate the effectiveness of the market model and the co-evolutionary algorithm. The results show that the secondary clearance mechanism is effective in increasing the rate of wind power consumption and improving the overall welfare of society.

Development of carbonized wood/silicon dioxide composite tailored for single-density shoe sole manufacturing

Response Surface Methodology (RSM) and the Normal Boundary Intersection (NBI) method were utilized in this study for modeling and multi-objective optimization of carbonized wood/silicon dioxide composites. Operational variables such as particle size, carbonization temperature, filler content, curing temperature, curing pressure, and curing time were simultaneously adjusted to improve the tensile, hardness, density, and water absorption characteristics of the composite. RSM models were developed utilizing an experimental approach based on the Central Composite Design (CCD). The models showed outstanding correlation coefficient, with a threshold of 0.9959 and was therefore employed as the fitness function for the NBI algorithm's multi-objective optimization. A set of optimal compromise solutions was found at 25 µm particle size, 200 °C carbonization temperature, 51 phr filler content, 150 °C curing temperature, 3 Pa curing pressure and 10 min curing time; this formular results in 19.22 MPa tensile stress, 1.08 g/cm3 density, 97.15 hardness (shore A) and 7.18% water absorption. The optimum material formular was then deployed in single-density shoe sole manufacturing using a predesigned steel mould. Fourier-transform infrared spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) were used to characterize the new material.

Many-objective rapid optimization of reactor shielding design based on NSGA - III

In this paper, a many-objective optimization method for reactor shielding design coupled with NSGA - III and neural network is proposed. By establishing the many-objective optimization model, using Monte Carlo method to calculate samples to train neural network, and the prediction results of neural network are used as the parameters of fitness function for many-objective optimization. The coupling between neural network and NSGA - III is realized, and the Pareto optimal solution of many-objective optimization of reactor shielding design is obtained. The results show that the method of neural network coupling NSGA - III performs well in solving the many-objective optimization problem, and can be applied to the many-objective optimization engineering design of reactor radiation shielding.

A hybrid computing approach to design the novel second order singular perturbed delay differential Lane-Emden model

In this study, the mathematical form of the second order perturbed singular delay differential system is presented. The comprehensive features using the singular-point, perturbed factor and pantograph term are provided together with the shape factor of the second order perturbed singular delay differential system. The novel model is simulated numerically through the artificial neural networks (ANNs) based on the global/local optimization procedures, i.e., genetic algorithm (GA) and sequential quadratic programming (SQP). An activation function is constructed by using the differential model based on the second order perturbed singular delay differential system. The optimization of fitness function is performed through the hybrid computing strength of the ANNs-GA-SQP to solve the second order perturbed singular delay differential system. The exactness, substantiation, and authentication of the novel system is observed to solve three different variants of the novel model. The convergence, robustness, correctness, and stability of the numerical approach is performed using the comparison procedures of the available exact solutions. For the reliability, the statistical performances with necessary processes are provided using the ANNs-GA-SQP.

The Application of Art Therapy Based on Particle Swarm Optimization Method for Preschool Children’s Mental Health

Art provides a starting point for children to understand works of art. In the initial stage of life, the development of vision and movement is particularly important, and painting helps coordinate the work of these systems. Art dispels the mystery of art and helps children connect with art classics, thus understanding the language and structure of art. In particle swarm optimization, the inertia weight is constant, but with the increase of iteration times, the inertia weight may become larger or smaller, so it tends to search globally or locally. This paper first analyzes what art therapy is from the basic concepts of art and art education and the stage characteristics of children’s painting and then studies the application of art therapy based on particle swarm optimization in preschool children’s art teaching. Finding the global optimal fitness function in the research process not only simplifies the computational complexity but also approximates the maximum coverage effect provided by the current network. The results show that applying particle swarm optimization algorithm to the research of mental health treatment of preschool children can establish an effective evaluation program in the practical research of children’s art treatment.

Improving Parameter Estimation of Fuel Cell Using Honey Badger Optimization Algorithm

In this study, we proposed an alternative method to determine the parameter of the proton exchange membrane fuel cell (PEMFC) since there are multiple variable quantities with diverse nonlinear characteristics included in the PEMFC design, which is specified correctly to ensure effective modeling. The distinctive model of FCs is critical in determining the effectiveness of the cells’ inquiry. The design of FC has a significant influence on the simulation research of such methods, which have been used in a variety of applications. The developed method depends on using the honey badger algorithm (HBA) as a new identification approach for identifying the parameters of the PEMFC. In the presented method, the minimal value of the sum square error (SSE) is applied to determine the optimal fitness function. A set of experimental series has been conducted utilizing three datasets entitled 250-W stack, BCS 500-W, and NedStack PS6 to justify the usage of the HBA to determine the PEMFC’s parameters. The results of the competitive algorithms are assessed using SSE and standard deviation metrics after numerous independent runs. The findings revealed that the presented approach produced promising results and outperformed the other comparison approaches.

Indices relation and statistical probability analysis of physical and mechanical performance of asphalt mixtures

Rutting deformation is one of the most prominent diseases in asphalt pavement’s early damage. In this paper, it analyzes the relation and their distribution law between Marshall stability and the physical and mechanical indices of asphalt mixtures based upon a correlation analysis and statistical principles, which is to find the reasonable range of rutting performance design parameters of asphalt pavement. The results showed that the asphalt mixture’s stability increased with the increase in bulk volume relative density and asphalt saturation, but decreased with the increase in the percent of air-voids and the voids in the mineral aggregate. The optimal fitting function of bulk volume relative density and asphalt saturation for AC-13 asphalt mixture follows a Weibull distribution, that of Marshall stability and air-voids follows a Gamma distribution, and that of percent voids in the mineral aggregate and flow value follows a Lognormal distribution. For a GAM-16 asphalt mixture, the optimal fitting function of bulk volume relative density and Marshall stability follows the Weibull distribution, the optimal fitting function of air-voids follows a Gamma distribution, that of flow value and percent voids in the mineral aggregate follows a Lognormal distribution, and asphalt saturation follows a Normal distribution. It is recommended that AC-13 asphalt mixtures’bulk volume relative density ranges from 2.27～2.38 g·cm3, the air-voids ranges from 3.4～6.0%, and GAM-16 asphalt mixtures’bulk volume relative density ranges from 2.29～2.37 g·cm3, the voids in mineral aggregate ranges from 17～18.17%, the asphalt saturation ranges from 75～82.13%, which can provide a reference for in-depth understanding of the deformation characteristics and construction of highways’ pavement in similar areas.

The underground explosion point high precision measurement method based on multidimensional information fusion of vibration sensors

PurposeThe purpose of this paper is to solve the problem that the location of the initiation point cannot be measured accurately in the shallow underground space, this paper proposes a method, which is based on fusion of multidimensional vibration sensor information, to locate single shallow underground sources.Design/methodology/approachFirst, in this paper, using the characteristics of low multipath interference and good P-wave polarization in the near field, the adaptive covariance matrix algorithm is used to extract the polarization angle information of the P-wave and the short term averaging/long term averaging algorithm is used to extract the first break travel time information. Second, a hybrid positioning model based on travel time and polarization angle is constructed. Third, the positioning model is taken as the particle update fitness function of quantum-behaved particle swarm optimization and calculation is performed in the hybrid positioning model. Finally, the experiment verification is carried out in the field.FindingsThe experimental results show that, with root mean square error, spherical error probable and fitness value as evaluation indicators, the positioning performance of this method is better than that without speed prediction. And the positioning accuracy of this method has been improved by nearly 30%, giving all of the three tests a positioning error within 0.5 m and a fitness less than 1.Originality/valueThis method provides a new idea for high-precision positioning of shallow underground single source. It has a certain engineering application value in the fields of directional demolition of engineering blasting, water inrush and burst mud prediction, fuze position measurement, underground initiation point positioning of ammunition, mine blasting monitoring and so on.

Optimization design and performance study of a subsoiler underlying the tea garden subsoiling mechanism based on bionics and EDEM

Considering the problems of high tillage resistance, high energy consumption, and failure to break the plow pan in subsoiling caused by the high viscosity of tea garden soil, a new design method of a subsoiler underlying the subsoiling mechanism based on structural bionics and the discrete element method is proposed. Taking the largest toe of mole cricket's forefoot as the bionic object, the biological information of the contour of mole cricket's toe was extracted using the methods of image processing and reverse engineering, and the edges of its outer and inner contours were obtained. Combined with the structure of an existing subsoiler, initial point cloud data of a bionic subsoiling shovel were mapped by the methods of proportional amplification and rotation. Taking the minimum sum of outer and inner contour fitting errors as the optimal objective function and selecting high-order polynomial as the fitting function, the optimization model of the outer and inner contour point cloud fitting function was established. The optimal fitting function of the outer and inner contour point cloud was obtained, and the bionic subsoiler model was established based on these two optimal functions. In the study of the discrete element, four soil particle models were proposed based on the actual shape of the cohesive soil of a tea garden. Based on the principle of discrete elements and the agronomic requirements, a modeling method of the plow pan soil was proposed. First, the adjacent soil particles at the bottom were connected by bonding, and the soil with large porosity was squeezed by the preset force to form the plow pan soil. Then, six tillage layers were added above the plow to form a soil structure model of the tea garden. The bionic subsoiler was assembled into a four-bar subsoiling mechanism. The process of the subsoiling mechanism acting on the soil was analyzed by the co-simulation of ADAMS-EDEM and compared with the common subsoiler. The results showed that the bionic subsoiler, compared with the common subsoiler, reduced the tillage force in the horizontal direction by 16.34% and in the vertical direction by 24.53%, reduced energy consumption by 9.64%, increased the damage to the plow pan by 5.10%, and caused greater disturbance to the internal soil. The influence of different driving speeds on the tillage performance was studied, which provides a theoretical reference for the better selection of the working speed for subsoiling.

Enhanced multiclass support vector data description model for fault diagnosis of gears

This work reports a study aimed at identifying the operating state of gear through an enhanced multiclass support vector data description (eMSVDD) model where the multiple hyperspheres corresponding to different operating states of gears are established. The model addresses the following issues: (1) the overlap of multiple hyperspheres deteriorates the diagnostic performance for boundary samples and severely weakens the generalization ability of the model; (2) the original support vector data description can only be applied to handle binary classification tasks; and (3) the performance of the SVDD depends heavily on the penalty factor C and kernel parameter δ. In the proposed model, we first implement the multi-label classification task by building multiple hyperspheres. Then, a novel fitness function for adaptive simplified chaotic particle swarm optimization is proposed to determine the model parameters, which considers the empirical risk minimization and model generalization capability. Finally, experimental results demonstrate that the eMSVDD outperforms deep neural networks and support vector machine by 3.4% and 0.5%, respectively, and this performance improvement respectively reaches 17.8% and 2.1% in the case of a very small sample size. The proposed approach provides a means for gear fault diagnosis with small samples.

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.

An Advance Computing Numerical Heuristic of Nonlinear SIR Dengue Fever System Using the Morlet Wavelet Kernel.

This study is associated to solve the nonlinear SIR dengue fever system using a computational methodology by operating the neural networks based on the designed Morlet wavelet (MWNNs), global scheme as genetic algorithm (GA), and rapid local search scheme as interior-point algorithm (IPA), i.e., GA-IPA. The optimization of fitness function based on MWNNs is performed for solving the nonlinear SIR dengue fever system. This MWNNs-based fitness function is accessible using the differential system and initial conditions of the nonlinear SIR dengue fever system. The designed procedures based on the MWNN-GA-IPA are applied to solve the nonlinear SIR dengue fever system to check the exactness, precision, constancy, and efficiency. The achieved numerical form of the nonlinear SIR dengue fever system via MWNN-GA-IPA was compared with the Runge–Kutta numerical results that verify the significance of MWNN-GA-IPA. Moreover, statistical reflections through different measures for the nonlinear SIR dengue fever system endorse the precision and convergence of the computational MWNN-GA-IPA.

FHSA: fractional harmony search algorithm for polyphase code design in radar signal processing

PurposeThe purpose of this research is to design and develop a technique for polyphase code design for the radar system.Design/methodology/approachThe proposed fractional harmony search algorithm (FHSA) performs the polyphase code design. The FHSA binds the properties of the harmony search algorithm and the fractional theory. An optimal fitness function based on the coherence and the autocorrelation is derived through the proposed FHSA. The performance metrics such as power, autocorrelation and cross-correlation measure the efficiency of the algorithm.FindingsThe performance metrics such as power, autocorrelation and cross-correlation is used to measure the efficiency of the algorithm. The simulation results show that the proposed optimal phase code design with FHSA outperforms the existing models with 1.420859, 4.09E−07, 3.69E−18 and 0.000581 W for the fitness, autocorrelation, cross-correlation and power, respectively.Originality/valueThe proposed FHSA for the design and development of the polyphase code design is developed for the RADAR is done to reduce the effect of the Doppler shift.

Improved cost-sensitive multikernel learning support vector machine algorithm based on particle swarm optimization in pulmonary nodule recognition

In the lung computer-aided detection (Lung CAD) system, the region of interest (ROI) of lung nodules has more false positives, making the imbalance between positive and negative (true positive and false positive) samples more likely to lead to misclassification of true positive nodules, a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm is proposed. Different penalty coefficients are assigned to positive and negative samples, so that the model can better learn the features of true positive nodules and improve the classification effect. To further improve the detection rate of pulmonary nodules and overall recognition accuracy, a score function named F-new based on the harmonic mean of accuracy (ACC) and sensitivity (SEN) is proposed as a fitness function for subsequent particle swarm optimization (PSO) parameter optimization, and a feasibility analysis of this function is performed. Compared with the fitness function that considers only accuracy or sensitivity, both the detection rate and the recognition accuracy of pulmonary nodules can be improved by this new algorithm. Compared with the grid search algorithm, using PSO for parameter search can reduce the model training time by nearly 20 times and achieve rapid parameter optimization. The maximum F-new obtained on the test set is 0.9357 for the proposed algorithm. When the maximum value of F-new is achieved, the corresponding recognition ACC is 91%, and SEN is 96.3%. Compared with the radial basis function in the single kernel, the F-new of the algorithm in this paper is 2.16% higher, ACC is 1.00% higher and SEN is equal. Compared with the polynomial kernel function in the single kernel, the F-new of the algorithm is 3.64% higher, ACC is 1.00% higher and SEN is 7.41% higher. The experimental results show that the F-new, ACC and SEN of the proposed algorithm is the best among them, and the results obtained by using multikernel function combined with F-new index are better than the single kernel function. Compared with the MKL-SVM algorithm of grid search, the ACC of the algorithm in this paper is reduced by 1%, and the results are equal to those of the MKL-SVM algorithm based on PSO only. Compared with the above two algorithms, SEN is increased by 3.71% and 7.41%, respectively. Therefore, it can be seen that the cost sensitive method can effectively reduce the missed detection of nodules, and the availability of the new algorithm can be further verified.

EEG CHANNEL SELECTION USING DIFFERANTIAL EVOLUTION ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR CLASSIFICATION OF ODORANT-STIMULATED RECORDS

A significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant-unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbor), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.

A sparsity-enhanced periodic OGS model for weak feature extraction of rolling bearing faults

The fault symptom of rolling bearings is usually characterized by transient impulses formed at equal intervals, but the impulse signal is easily affected by noise and harmonic interferences, which increases the difficulty of extracting impulse features. In order to realize the effective extraction of weak periodic impulses under strong noise, this paper constructs a non-convex penalty function based on elastic net and Lp norm, and proposes a sparsity-enhanced periodic overlapping group shrinkage (POGS) method to detect rolling bearing faults. In the proposed sparse model, the internal function of the non-convex penalty function adopts the period-guided elastic net group sparse constraint, and the envelope autocorrelation function is used to dynamically update the period prior information to improve the extraction accuracy of highly correlated features within the group. Meanwhile, the non-convex Lp norm is introduced into the penalty function to constrain the sparsity of the overall variables, so as to guide the sparsity within and across groups (SWAG) of faults features while maintaining the weak impulse amplitudes. A comprehensive evaluation indicator is constructed as the fitness function of the moth-flame optimization (MFO) algorithm to realize automatic selection of model parameters. On the basis of the majorization-minimization (MM) algorithm and the improved soft threshold algorithm, the process of solving the objective function of the proposed model is given, and the performance of the proposed method is analyzed. The analysis results of the experimental data of rolling bearings suggest that in comparison with some existing sparse denoising methods, the proposed method exhibits better performance in the extraction of weak periodic impulses.

Evaluation of axis straightness error of shaft and hole parts based on improved grey wolf optimization algorithm

Currently, it has been considered a nonlinear optimization problem to accurately evaluate axis straightness error of shaft and hole parts. Using intelligent optimization algorithm to solve this problem can avoid complex mathematical modeling process, while showing the advantages of high solution accuracy, fast search speed and easy convergence. By using the grey wolf optimization (GWO) algorithm with strong convergence performance, the global search performance was improved by regulating the linear convergence factor to nonlinear, and the wolf in the optimal position was endowed with the capability of receiving information and moving autonomously. Thus, an improved grey wolf optimization (IGWO) algorithm with better optimization accuracy was yielded. Moreover, the fitness function of optimization was rebuilt, thereby avoiding the unscientific setting of the parameter optimization range based on the subjective experience. Lastly, IGWO was successfully applied to the evaluation of axis straightness error of shaft and hole parts with good accuracy.

Nonstationary signal extraction based on BatOMP sparse decomposition technique

Sparse decomposition technique is a new method for nonstationary signal extraction in a noise background. To solve the problem of accuracy and efficiency exclusive in sparse decomposition, the bat algorithm combined with Orthogonal Matching Pursuits (BatOMP) was proposed to improve sparse decomposition, which can realize adaptive recognition and extraction of nonstationary signal containing random noise. Two general atoms were designed for typical signals, and dictionary training method based on correlation detection and Hilbert transform was developed. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with several relevant methods, it was indicated that BatOMP can improve convergence speed and extraction accuracy efficiently as well as decrease the hardware requirement, which is cost effective and helps broadening the applications.

A study on the construction of O2O teaching system of college English cross-cultural knowledge based on MOOC artificial intelligence

In this paper, MOOC-based O2O Teaching System for college English cross - cultural knowledge is introduced to improve the 3P model of traditional English teaching through data analysis of the combined and optimized fitness function and social evolution operator. First, through analyzing and summarizing a large number of domestic and foreign literatures and research results, the construction scheme of MOOC-based O2O Teaching System for college English cross-cultural knowledge was established. Moreover, this paper studies the construction mode of MOOC-based O2O Teaching System for college English cross-cultural knowledge and finally establishes the teaching system, and concludes that colleges and universities should pay attention to the cultural difference in English learning, and emphasis on the cultivation of college student cross-cultural knowledge. The specific path is online education combined with offline education for common development, thereby enhancing college students’ English learning ability and practical ability. To sum up, in college English teaching, we should pay attention to the education of English cross-cultural knowledge, O2O mode for cross-cultural knowledge can be used to combine 3P teaching mode and online education mode in MOOC environment, promoting innovation and development of educational methods under the guidance of the constructivism, so as to improve students’ English proficiency.