Hardware implementations of Swarming Intelligence &amp;#x2014; a survey

Johnson, Cameron; Venayagamoorthy, G.K.; Palangpour, Parviz

doi:10.1109/sis.2008.4668331

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Besides, the proposed CAR-BBO belongs to the class of swarm intelligence (SI) algorithms, which are ideal for parallelization since there are no requirements for central processing. 60…”

Section: Discussionmentioning

confidence: 99%

Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization

Zhang

et al. 2016

SIMULATION

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In this study, we proposed a smart detection method for abnormal breasts in digital mammography. Firstly, preprocessing was carried out to deaden noises, enhance images, and remove background and pectoral muscles. Secondly, fractional Fourier entropy was employed to extract global features. Thirdly, the Welch’s t-test was utilized to select important features. Fourthly, the multi-layer perceptron was used as the classifier. Finally, we proposed a novel chaotic adaptive real-coded biogeography-based optimization to train the classifier. We implemented 10-fold cross-validation for statistical analysis. The experimental results showed our method selected in total 23 distinguishing features, and yielded a sensitivity of 92.54%, a specificity of 92.50%, a precision of 92.50%, and an accuracy of 92.52%. This proposed system performs better than five state-of-the-art methods. It is effective in abnormal breast detection.

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“…Besides, the proposed CAR-BBO belongs to the class of swarm intelligence (SI) algorithms, which are ideal for parallelization since there are no requirements for central processing. 60…”

Section: Discussionmentioning

confidence: 99%

Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization

Zhang

et al. 2016

SIMULATION

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“…This feature makes it easy to design an algorithm. Some implementations of PSO in electronic circuits have been studied [36,37]. The proposed method would be suitable for hardware implementation because of the low number of system parameters and the lack of random values.…”

Section: Introductionmentioning

confidence: 99%

An optimizer using swarm of chaotic dynamical particles

Tsubone

2015

NOLTA

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A novel optimization method called, "optimizer using swarm of chaotic dynamical particles," (OSCDP) is proposed. The proposed method searches for an optimal solution on the basis of particles governed by chaotic dynamics. The chaotic dynamics make the particles behave in a complex way, by stretching and holding mechanisms, even though the dynamics do not contain any stochastic terms. The particles share information of their best position that is a candidate of the optimal solution, as with particle swarm optimization (PSO). The complex behavior and the sharing information mechanism enable the proposed method to search for the optimal solution. This method consists of only three system parameters, making it suitable for implementing the algorithm. We compared the proposed method with other deterministic PSOs and the standard PSO, and found that the proposed method exhibited a better searching performance.

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