Concurrent designing of tolerance has become a vital concern in product and process development due to the relationship between quality, functionality and product cost. It is one of the well explored areas in combinat...
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Concurrent designing of tolerance has become a vital concern in product and process development due to the relationship between quality, functionality and product cost. It is one of the well explored areas in combinatorial optimization. In this paper, a recently developed optimization algorithm, called bat algorithm (BA), is used for optimizing the tolerance based on concurrent objectives to minimize the manufacturing cost, present worth of expected quality loss and quality loss. The mechanical assemblies such as Bevel gear assembly (A), Gear box assembly (B) and Suction union assembly (C) are considered to demonstrate the proposed algorithm. It is found that the BA has produced better results than other methods in initial generations for concurrent tolerance problems.
In this paper we propose a new method for dynamic parameter adaptation in the bat algorithm (BA). BA is a metaheuristic algorithm inspired by the behavior of micro bats, which has been applied to different optimizatio...
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ISBN:
(纸本)9781509013531
In this paper we propose a new method for dynamic parameter adaptation in the bat algorithm (BA). BA is a metaheuristic algorithm inspired by the behavior of micro bats, which has been applied to different optimization problems obtaining good results. In this paper we propose dynamic parameter adaptation of the BA using Interval Type-2 fuzzy logic. Simulation results show that the proposed method using Type-2 fuzzy logic is better in comparison with Type-1 fuzzy logic.
In biometric system, gait based recognition has more challenges as a result of changing appearance by several factors. To overcome these challenges, in our previous work, effective gait recognition using multi-objecti...
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ISBN:
(纸本)9781509045594
In biometric system, gait based recognition has more challenges as a result of changing appearance by several factors. To overcome these challenges, in our previous work, effective gait recognition using multi-objective enhanced adaptive fusion technique by bat algorithm (EGRMEAFbat) was proposed in which the most efficient features, most informative less efficient features and shape features from a gait sequence of an individual and gender identification were utilized to recognize the person. In this paper, we propose a novel fusion technique for efficient gait recognition named as efficient gait recognition using multi-objective effective enhanced adaptive fusion by bat algorithm (EGRME(2)AFbat). The proposed method is performed by considering additional features such as velocity moment and depth of both hands and legs along with features utilized in EGRMEAFbat. Here, velocity moments and depth are measured by velocity measurement and feature vector calculation method respectively. Then, the extracted features are effectively fused using multi-objective effective enhanced adaptive fusion by bat algorithm and the fused features are used for classification to recognize the person. The experimental results demonstrate that the proposed technique provides better accuracy compared to the gait recognition by EGRMEAFbat.
Carbon pricing is regarded as a crucial enabler for an accelerated low-carbon energy economy transformation to achieve temperature control targets. This paper studies carbon price forecasting considering historical ca...
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Carbon pricing is regarded as a crucial enabler for an accelerated low-carbon energy economy transformation to achieve temperature control targets. This paper studies carbon price forecasting considering historical carbon price series as an influencing factor. A hybrid model of a kernel-based extreme learning machine (KELM) optimized by the bat optimization algorithm based on wavelet transform is proposed. Firstly, the wavelet transform is used to eliminate the high-frequency components of the previous day's carbon price data to improve the accuracy of prediction. Then, the partial auto-correlation function (PACF) is applied to analyse the correlation among historical carbon prices to select the inputs for the forecasting model. Additionally, adding a kernel function improves to some extent the fitting accuracy and stability of the traditional extreme learning machine. Finally, the parameters of the KELM model are optimized by the bat optimization algorithm. Two types of carbon prices in the China ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results show that the proposed hybrid methodology is more robust than other comparison models for carbon price forecasting.
Association rule mining meeting a variety of measures is regarded as a multi-objective optimization problem rather than a single objective optimization problem. The convergent speed of traditional multi-objective algo...
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Association rule mining meeting a variety of measures is regarded as a multi-objective optimization problem rather than a single objective optimization problem. The convergent speed of traditional multi-objective algorithms such as genetic algorithm is slow and the efficiency of these algorithms is low. Furthermore, the rules generated by traditional multi-objective algorithms are too large to be efficiently analyzed and explored in any further process. bat algorithm is a new efficient global optimal algorithm whose convergence is superior to binary particle swarm optimization (BPSO) and genetic algorithm. This paper discusses the application of multi-objective bat algorithm to association rule mining. We propose multi-objective binary bat algorithm (MBBA) based on Pareto for association rule mining. This algorithm is independent of minimum support and minimum confidence. To evaluate the association rules mined by MBBA algorithm, we propose a new method to discover interesting association rules without favoring or excluding any measure. Compared with the single-objective BPSO, binary bat algorithm (BBA) and Apriori algorithm, the experimental results on six datasets show that the new algorithm is feasible and highly effective. It can make up the shortage of single objective algorithms and traditional association rule mining algorithms.
bat algorithm is proposed in this paper for optimal tuning of PI controllers for load frequency controller (LFC) design. The problem of robustly tuning of PI based LFC design is formulated as an optimization problem a...
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bat algorithm is proposed in this paper for optimal tuning of PI controllers for load frequency controller (LFC) design. The problem of robustly tuning of PI based LFC design is formulated as an optimization problem according to time domain objective function that is solved by bat algorithm to find the most optimistic results. To demonstrate the effectiveness of the proposed method, a two-area interconnected power system is considered as a tested system. To ensure robustness of the proposed control strategy to stabilize frequency oscillations, the design process takes a wide range of operating conditions and system nonlinearities into account. The simulation results are given to detect the superiority of bat algorithm over Simulated Annealing (SA) in tuning PI controller parameters through different indices. Results evaluation show that the proposed algorithm achieves good robust performance for wide range of system parameters and load changes compared with SA. (C) 2015 Elsevier Ltd. All rights reserved.
An improved bat algorithm is proposed for solving the job shop scheduling ***, introduce the characteristic of job shop scheduling problem, Then, we use the advantage of chaos method and combine the simulated annealin...
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An improved bat algorithm is proposed for solving the job shop scheduling ***, introduce the characteristic of job shop scheduling problem, Then, we use the advantage of chaos method and combine the simulated annealing algorithm to improve the bat algorithm, and the end, We do a lot of experiments and evaluate the performance of the improved bat algorithm. Compared with the standard bat algorithm, the superiority of the improved bat algorithm is verified.
Considering the problems of low solution precision and suffering from pre-mature convergence by the basic bat algorithm, an improved self-adaptive bat algorithm(SABA) is proposed after the introduction of the self-ada...
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Considering the problems of low solution precision and suffering from pre-mature convergence by the basic bat algorithm, an improved self-adaptive bat algorithm(SABA) is proposed after the introduction of the self-adaptive step-controlled mechanism and mutation mechanism in this paper. The results of the proposed algorithm based on 6 test functions indicate that compared with the particle swarm algorithm and the basic bat algorithm, the SABA algorithm effectively avoid falling into the local optimum in the early stage and had the higher solution accuracy.
The region of interest(ROI) segmentation of infrared image is one of the key steps in intelligent fault diagnosis of power equipment. Subsequently, a two-dimensional entropy multilevel threshold based on bat algorithm...
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The region of interest(ROI) segmentation of infrared image is one of the key steps in intelligent fault diagnosis of power equipment. Subsequently, a two-dimensional entropy multilevel threshold based on bat algorithm is proposed. By analyzing the multilevel threshold segmentation principle with two-dimensional entropy, the bat algorithm is used to search the optimal segmentation threshold, and these thresholds are used to the threshold segmentation experiment on the infrared image of the power equipment. The results show that this method improves time consuming of the algorithm and the precision of image segmentation compared with the multi-threshold segmentation method with two-dimensional entropy based on particle swarm optimization algorithm, which effectively solves the problem of multilevel threshold segmentation of infrared image. It lays the foundation for the extraction and analysis of temperature field characteristics of follow-up equipment, and is more suitable for the intelligent diagnosis of infrared images of power equipment failure.
Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart...
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Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.
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