A new clustering-based routing method in the mobile internet of things using a krill herd algorithm

The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.

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Improved Krill Herd Algorithm based Sliding Mode MPPT Controller for Variable Step Size P&O method in PV system under Simultaneous Change of Irradiance and Temperature

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2021.02.021 ◽

2021 ◽

Author(s):

Van-Quang-Binh Ngo ◽

Mohsen Latifi ◽

Rabeh Abbassi ◽

Houssem Jerbi ◽

Kentaro Ohshima ◽

...

Keyword(s):

Sliding Mode ◽

Variable Step Size ◽

Step Size ◽

Pv System ◽

Krill Herd Algorithm ◽

Krill Herd ◽

Variable Step ◽

Mppt Controller ◽

Simultaneous Change

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Multi-objective optimization of optimal placement and sizing of multiple DG placements in radial distribution system using stud krill herd algorithm

Neural Computing and Applications ◽

10.1007/s00521-021-05992-x ◽

2021 ◽

Author(s):

S. A. Chithra Devi ◽

K. Yamuna ◽

M. Sornalatha

Keyword(s):

Radial Distribution ◽

Distribution System ◽

Optimal Placement ◽

Multi Objective Optimization ◽

Radial Distribution System ◽

Multi Objective ◽

Krill Herd Algorithm ◽

Krill Herd

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HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽

10.3390/s20030660 ◽

2020 ◽

Vol 20 (3) ◽

pp. 660 ◽

Cited By ~ 1

Author(s):

Fang Liu ◽

Liubin Li ◽

Yongbin Liu ◽

Zheng Cao ◽

Hui Yang ◽

...

Keyword(s):

Kernel Method ◽

Back Propagation ◽

Vibration Signal ◽

Back Propagation Neural Network ◽

Industrial Applications ◽

Performance Degradation ◽

Support Vector ◽

Krill Herd Algorithm ◽

Krill Herd ◽

Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

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