Probabilistic opposition-based particle swarm optimization with velocity clamping

Shahzad, Farrukh; Masood, Sohail; Khan, Naveed Kazim

doi:10.1007/s10115-013-0624-z

Cited by 39 publications

(22 citation statements)

References 27 publications

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“…We cite for instance its use with differential evolution [66][67][68], artificial neural networks [69], particle swarm optimisation [70][71][72][73], bio-geography optimisation [74], ant colonies [75] and simulated annealing [76]. These hybridisations and others are well detailed in [67].…”

Section: Opposition-based Learningmentioning

confidence: 99%

On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

Draa

2015

Applied Soft Computing

108

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Section: Opposition-based Learningmentioning

confidence: 99%

On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

Draa

2015

Applied Soft Computing

108

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“…"Velocity clamping" [29] was used to set an upper bound for the velocity parameter as a way to limit particles flying out of the search space.…”

Section: Psomentioning

confidence: 99%

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Zhang

Yang

et al. 2016

THC

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Abstract. It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-behaved particle swarm optimization (QPSO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed WE + QPSO-KSVM was superior to "DWT + PCA + BP-NN", "DWT + PCA + RBF-NN", "DWT + PCA + PSO-KSVM", "WE + BPNN", "WE + KSVM", and "DWT + PCA + GA-KSVM" w.r.t. sensitivity, specificity, and accuracy. The work provides a novel means to detect abnormal brains with excellent performance.

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“…"Velocity clamping" [48] was used as a way to limit particles flying out of the search space. Another method is the "constriction coefficient" strategy, proposed by Clerc and Kennedy [49], as an outcome of a a theoretical analysis of swarm dynamic, in which the velocities are constricted too.…”

Section: Algorithm -Psomentioning

confidence: 99%

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

Zhang¹,

Wang²,

Dong³

et al. 2015

PIER

110

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Abstract-(Background)We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.

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Probabilistic opposition-based particle swarm optimization with velocity clamping

Cited by 39 publications

References 27 publications

On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

On the performances of the flower pollination algorithm – Qualitative and quantitative analyses

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

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