Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement

Ma, Jun; Pereira, João Luiz Junho; Oliva, Diego; Liu, Shuai; Kuo, Yong-Hong

doi:10.1016/j.knosys.2022.110247

Cited by 25 publications

(8 citation statements)

References 65 publications

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“…[43]. At the same time, some new algorithms with excellent performance have emerged, such as Atom Search Optimization (ASO) [44], Manta Ray Foraging Optimization (MRFO) [45], Slime Mold Algorithm (SMA) [46]. Zhang et al proposed a Generalized Normal Distribution Optimization algorithm (GNDO) and applied it to the key parameters' extraction of photovoltaic models [39].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Mao,

Zhou,

et al. 2024

Front. Phys.

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To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Mao,

Zhou,

et al. 2024

Front. Phys.

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“…In 2020, Zhao was inspired by the foraging law of marine manta rays and proposed a swarm intelligence algorithm called MRFO 17 . Today, MRFO has developed many improved versions and involves many practical applications, such as the binary version of MRFO, 18 the multiobjective version of MRFO, 19 engineering, 19 economic scheduling, 20 and image segmentation 21 …”

Section: Introductionmentioning

confidence: 99%

“…17 Today, MRFO has developed many improved versions and involves many practical applications, such as the binary version of MRFO, 18 the multiobjective version of MRFO, 19 engineering, 19 economic scheduling, 20 and image segmentation. 21 The research focus of this paper is to solve the power allocation problem of maximizing confidentiality rate in optimal resource allocation of wireless communication through MRFO, 22 the trade-off between energy efficiency and SE 23 in interference limited wireless networks, and mobile edge computing (MEC) migration. 24 In order to effectively solve the above problems, on the basis of MRFO, the ability of the original algorithm to jump out of the local optimal is enhanced with the help of the mutation strategy of the genetic algorithm, and spiral modeling is introduced as the first stage to promote the optimization speed and accelerate the convergence speed in the later stage, and IMRFO is proposed.…”

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confidence: 99%

A spiral modeling based on manta ray foraging optimization for wireless networks resource allocation

Luo,

Wang,

Zhou

2024

Concurrency and Computation

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SummaryThe wireless network resource allocation is a NP‐hard combinatorial optimization problem. This paper proposes a new optimization method using the manta ray foraging optimization (MRFO) based on spiral modeling to solve the wireless network resource problem. In order to reduce the computational complexity and ensure the optimal performance of the allocation scheme, a MRFO algorithm based on spiral modeling and mutation strategy is proposed. In the first stage, spiral modeling is introduced to narrow the exploration area, while the mutation strategy of the genetic algorithm enhances the ability of the algorithm to jump out of the local optimal. In the second stage, a binary MRFO algorithm for using different transfer functions is proposed to solve the mixed integer programming problem. The experimental results show that IMRFO and BIMRFO have high comprehensive performance advantages, achieve better effects than the other optimization algorithms, which lead to faster convergence, faster accuracy, and lower complexity.

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“…Although MRFO has been applied in many fields, previous studies have shown that its exploration ability is weak [ 32 ], and it is easy to stagnate the local optimum [ 33 ]. In order to solve this problem, this paper improves MRFO and applies it to WSN node deployment on a three-dimensional surface.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

Wang,

Luo,

Wei

et al. 2023

Biomimetics

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In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

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Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement

Cited by 25 publications

References 65 publications

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

A spiral modeling based on manta ray foraging optimization for wireless networks resource allocation

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

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