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Grey wolf optimization-based variational mode decomposition for magnetotelluric data combined with detrended fluctuation analysis

Zhang Xian, Li Diquan, Li Jin, Li Yong

Acta Geophysica

2022

Vol. 70, no 1

111--120

We present a magnetotelluric data denoising method that uses grey wolf optimization to optimize variational mode decomposition and combines it with detrended fluctuation analysis. First, envelope entropy is selected as the fitness function for grey wolf optimization and is used to determine the number of modes K and the penalty factor, which are the key parameters of the variational mode decomposition method. Then, the optimized variational mode decomposition method is used to decompose magnetotelluric data. Finally, the scaling exponent in detrended fluctuation analysis is used to determine the corresponding intrinsic mode function components to superimpose and reconstruct the useful magnetotelluric data. Extensive experiments and thorough analyses are performed on the synthetic data and field data. The results of the proposed method are compared with the results of the remote reference, variational mode decomposition, variational mode decomposition and matching pursuit, variational mode decomposition and detrended fluctuation analysis methods; the proposed method can improve the denoising performance and reliability of low-frequency magnetotelluric data. The reconstructed data are closer to the natural magnetotelluric data. The satisfactory performance in the results verifies the effectiveness of the design and optimization method.

Global path planning for multiple AUVs using GWO

Panda Madhusmita, Das Bikramaditya, Pati Bibhuti

Archives of Control Sciences

2020

Vol. 30, No. 1

77--100

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.