To improve the computation and real-time performances of the multiple signal classification (MUSIC) algorithm in 3D space, a fast sound source localization method based on the bat algorithm (BA) and the 3D-MUSIC, call...
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To improve the computation and real-time performances of the multiple signal classification (MUSIC) algorithm in 3D space, a fast sound source localization method based on the bat algorithm (BA) and the 3D-MUSIC, called BA-based 3D-MUSIC algorithm (3D-BMUSIC), is presented in this paper. 3D-BMUSIC greatly reduces the computation load by replacing the regular grid search with the BA. First, the near-field spherical wave model is established to obtain the spectral function of the 3D-MUSIC. Then, the spectral function is defined as the fitness function, which calculates the fitness value corresponding to each bat position. Finally, the global optimal bat position with the largest fitness value, as sound source localization, is obtained by successive iteration and sorting. The simulation and experiment show that 3D-BMUSIC accurately estimates the DOA and distance of near-field sources, and the root-mean-square error (RMSE) of 3D-BMUSIC is less than that of 3D-MUSIC. In addition, 3D-BMUSIC effectively reduces the computation time by approximately 96-98%. With shorter computation time and higher efficiency, 3D-BMUSIC promotes hardware implementation and is more suitable for high-precision localization of near-field sound sources.
In the production and processing of precision shaft-hole class parts, the wear of cutting tools, machine chatter, and insufficient lubrication can lead to changes in their roundness, which in turn affects the overall ...
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In the production and processing of precision shaft-hole class parts, the wear of cutting tools, machine chatter, and insufficient lubrication can lead to changes in their roundness, which in turn affects the overall performance of the relevant products. To improve the accuracy of roundness error assessments, bat algorithm (BA) is applied to roundness error assessments. An improved bat algorithm (IBA) is proposed to counteract the original lack of variational mechanisms, which can easily lead BA to fall into local extremes and induce premature convergence. First, logistic chaos initialisation is applied to the initial solution generation to enhance the variation mechanism of the population and improve the solution quality;second, a sinusoidal control factor is added to BA to control the nonlinear inertia weights during the iterative process, and the balance between the global search and local search of the algorithm is dynamically adjusted to improve the optimization-seeking accuracy and stability of the algorithm. Finally, the sparrow search algorithm (SSA) is integrated into BA, exploiting the ability of explorer bats to perform a large range search, so that the algorithm can jump out of local extremes and the convergence speed of the algorithm can be improved. The performance of IBA was tested against the classical metaheuristic algorithm on eight benchmark functions, and the results showed that IBA significantly outperformed the other algorithms in terms of solution accuracy, convergence speed, and stability. Simulation and example verification show that IBA can quickly find the centre of a minimum inclusion region when there are many or few sampling points, and the obtained roundness error value is more accurate than that of other algorithms, which verifies the feasibility and effectiveness of IBA in evaluating roundness errors.
System-on-Chip (SoC) is a structure in which semiconductor components are integrated into a single die. As a result, testing time should be reduced to achieve a low cost for each chip. Effective test scheduling can re...
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System-on-Chip (SoC) is a structure in which semiconductor components are integrated into a single die. As a result, testing time should be reduced to achieve a low cost for each chip. Effective test scheduling can reduce the SoC testing time, which is more challenging due to its complexity. In this paper, the modified bat algorithm-based test scheduling is proposed. Testing is carried out on the SoC ITC'02 benchmark circuits. The Modified bat method is a recently heuristic algorithm that performs global optimization by imitating bat echolocation. Compared to other state-of-the-art algorithms, the Modified bat Optimization method reduces testing time on SoCs. This paper improves the algorithm's exploration process by adjusting the equation for bat loudness (A(0)) and pulse emission rate (r). The modified bat algorithm converges to the optimal solution faster. It has been used in 14 international standard test functions. The test results indicate that the modified bat algorithm has a fast convergence speed, which minimizes the testing time compared to other evolutionary algorithms on the ITC'02 SoC benchmark circuits.
Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants' credentials and directly affect...
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Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants' credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive performance of credit scoring model. Towards to feature selection, Binary bat optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with "Radial Basis Function Neural Network (RBFN)", "Support Vector Machine (SVM)" and "Random Forest (RF)" for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring.
Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes *** has marked its importance in both practical and research *** is the most significant resource,the imp...
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Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes *** has marked its importance in both practical and research *** is the most significant resource,the important challenge in WSN is to extend its *** energy reduction is a key to extend the network’s *** of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving *** this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based bat algorithm(ECHO-bat).ECHO-bat works in two *** Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using bat *** second stage aims to find the nodes if any,with high residual energy within each *** will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s *** performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering algorithm(PEZCA)and Chaotic Firefly algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution *** results show that ECHO-bat outperforms the other methods in all the considered *** overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.
In order to improve the accuracy of integrated energy microgrid cluster scheduling operation in existing technologies, a scheduling optimization measure based on an improved bat algorithm was proposed in integrated en...
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In order to improve the accuracy of integrated energy microgrid cluster scheduling operation in existing technologies, a scheduling optimization measure based on an improved bat algorithm was proposed in integrated energy system in this study. Firstly, the integrated demand response (IDR) was used to enhance the load-side energy utilization link. Secondly, the front-end data fragments of the integrated energy system are optimized and integrated based on fuzzy clustering and Mahalanobis distance. Finally, the bat algorithm is improved and substituted by increasing the adaptive inertia weight coefficient. In the simulation stage, the operation simulation test of the system was realized by MATLAB. Experiments show that the optimized integration method based on fuzzy clustering and Mahalanobis distance can make the front-end data achieve reliable clustering, and reduce the redundancy by more than 80%. After improving the bat algorithm, not only the MAPE and RMSE error of scheduling prediction can be reduced to 1.52% and 17.45%, but also the total cost of system scheduling operation can be reduced by about 2.5%. This study can improve the accuracy, economy and prediction speed of scheduling prediction, and has a good application prospect in the field of integrated energy scheduling optimization. (C) 2022 The Authors. Published by Elsevier Ltd.
bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization *** leverages the echolocation feature of bats produced by imitating the bats’searching *** fac...
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bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization *** leverages the echolocation feature of bats produced by imitating the bats’searching *** faces premature convergence due to its local search *** of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search *** this work,we proposed an improved variation of BA by applying torus walk to improve diversity and *** *** Computerized bat algorithm(MCBA)approach has been examined for fifteen well-known benchmark test *** finding of our technique shows promising performance as compared to the standard PSO and standard *** proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of *** results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional *** MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.
This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of th...
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This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the AdaBoost model (i.e., the maximum number of iterations (n) and the weight reduction coefficient (v) of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-AdaBoost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-AdaBoost combined model can better search for globally optimal parameters. The AdaBoost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g . kg(-1), respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-AdaBoost model has certain application prospects in field soil nutrient estimation.
Regression testing is essential for continuous integration and continuous development. It is needed to ensure that the modifications have not produced any errors or faults, thereby maintaining the quality and reliabil...
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Regression testing is essential for continuous integration and continuous development. It is needed to ensure that the modifications have not produced any errors or faults, thereby maintaining the quality and reliability of the software. The testers usually avoid exhaustive retesting because it requires lots of effort and time. The test case prioritization and minimization solve the issue by scheduling the critical test cases and removing redundant ones. Optimization techniques help by improving the efficiency of these techniques while utilizing limited resources. This paper proposed an enhanced discrete novel bat algorithm for the test case prioritization. The algorithm is modified in two ways. First, we have proposed a fix-up mechanism for the discrete combinatorial problem, which conducts the perturbation in the population using the asexual reproduction algorithm. Second, the novel bat algorithm is improved, where the bats hunt in different habitats with quantum behavior using Gaussian distribution and search in the limited habitat with Doppler effect. In addition, we have embedded the test case minimization procedure in the algorithm for redundancy reduction. The experimental results are empirically analyzed using different testing criteria, i.e., fault and statement coverage on three subject programs from the software infrastructure repository. Consequently, test selection percentage, coverage loss, fault detection loss, and cost reduction percentages are deduced for the test case minimization at program and version levels. Empirical results and statistical comparisons with the random search, bat algorithm, novel bat algorithm, birds swarm algorithm, whale optimization algorithm, and genetic algorithm show the outperformance of the proposed algorithm.
Under partial shading conditions (PSC), most traditional maximum power point tracking (MPPT) techniques may not adopt GP (global peak). These strategies also often take a considerable amount of time to reach a full po...
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Under partial shading conditions (PSC), most traditional maximum power point tracking (MPPT) techniques may not adopt GP (global peak). These strategies also often take a considerable amount of time to reach a full power point (MPP). Such obstacles can be eliminated by the use of metaheuristic strategies. This paper shows, in partial shading conditions, the MPPT technique for the photovoltaic system using the bat algorithm (BA). Simulations have been performed in the MATLAB (R)/Simulink setting to verify the efficacy of the proposed method. In MPPT applications, the results of the simulations emphasize the precision of the proposed technique. The algorithm is also simple and efficient, on a low-cost microcontroller, it could be implemented. Hardware in Loop (HIL) validation is performed, with a Typhoon HIL 402 setup.
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