A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance Between Exploration and Exploitation

Nguyen, Bach Hoai; Xue, Bing; Andreae, Peter; Zhang, Mengjie

doi:10.1109/tcyb.2019.2944141

Cited by 85 publications

(32 citation statements)

References 43 publications

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“…According to [19], the inertia weight value plays an important role in balancing exploration and exploration in search space. The linear time-varying inertia weight is applied in this study as shown in ( 7), its value decreases from 0.9 to 0.4 according to the number of iteration [33].…”

Section: Features From Frequency Domainmentioning

confidence: 99%

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Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

Lee

2020

IEEE Access

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This paper presents an effective bearing fault diagnosis model based on multiple extraction and selection techniques. In multiple feature extraction, the discrete wavelet transform, envelope analysis, and fast Fourier transform are considered. While the combined binary particle swarm optimization with extended memory is focusing on feature selection. The current signals are analyzed by discrete wavelet transform. From there, the statistical features in the time and frequency domain are extracted by two techniques: envelope analysis, fast Fourier transform. Subsequently, the binary particle swarm optimization is combined with extended memory and two proposed position update mechanisms to eliminate redundant or irrelevant features to achieve the optimal feature subset. Besides, three classifiers including naïve Bayes, decision tree, and linear discriminant analysis are applied and compared to select the best model to detect the bearing fault.

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Section: Features From Frequency Domainmentioning

confidence: 99%

“…It is converted into the binary value for the corresponding position by the transfer function. In the binary search space, new solutions are created by flipping their position entries [19]. Therefore, there is no guarantee that the new positions after going through the update process do not coincide with the previous ones.…”

Section: Introductionmentioning

confidence: 99%

Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

Lee

2020

IEEE Access

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“…The continuous search step uses the conventional PSO method, and the binary search step is implemented as the primary search strategy and uses a "stickiness" momentum mechanism in BPSO. The term "stickiness" was proposed by Nguyen et al [55,57]. Since applying the speed concept of PSO directly to BPSO is not appropriate, the main idea of "stickiness" is that a particle moves by flipping its position entry in BPSO.…”

Section: Mixed Pso Algorithmmentioning

confidence: 99%

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

et al. 2020

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In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

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“…Particle swarm optimization (PSO) is an evolutionary computation technology [ 12 ], which is derived from the research on the behavior of bird predation—a random search algorithm based on group collaboration developed by simulating the behavior of bird foraging. PSO can deal with some problems that cannot be dealt with by traditional methods, such as the lack of gradient information, but its performance is flawed, so someone proposed the binary PSO (BPSO) [ 13 ]. In the BPSO algorithm, each dimension of each particle is taken as a binary discrete value, namely, 0 or 1, and there is no limit to speed.…”

Section: Introductionmentioning

confidence: 99%

Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm‐Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor

Tian²,

Wang

et al. 2022

Contrast Media & Molecular Imaging

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This research was aimed to explore the application value of magnetic resonance imaging (MRI) based on binary particle swarm optimization algorithm (BPSO) in the diagnosis of adrenal tumors. 120 patients with adrenal tumors admitted to the hospital were selected and randomly divided into the control group (conventional MRI examination) and the observation group (MRI examination based on the BPSO intelligent feature optimization algorithm), with 60 cases in each group. The sensitivity, specificity, accuracy, and Kappa of the diagnostic methods were compared between the two groups. The results showed that the calculation rate of the BPSO algorithm was the best under the same processing effect ( P < 0.05). Optimization algorithm-based MRI is used in the diagnosis of adrenal tumors, and the results showed that the sensitivity, specificity, accuracy, and Kappa (83.33%, 79.17%, 81.67%, and 0.69) of the observation group were higher than those of the control group (50%, 75%, 58.33%, and 0.45). The similarity of tumor location results in the observation group (89.24%) was significantly higher than that in the control group (65.9%) ( P < 0.05). In conclusion, compared with SFFS and other algorithms, the BPSO algorithm has more advantages in calculation speed. MRI based on the BPSO intelligent feature optimization algorithm has a good diagnostic effect and higher accuracy in adrenal tumors, showing the good development prospects of computer intelligence technology in the field of medicine.

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A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance Between Exploration and Exploitation

Cited by 85 publications

References 43 publications

Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory

A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm‐Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor

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