Fast artificial bee colony and its application to stereo correspondence

Hong, Phuc Nguyen; Ahn, Chang Wook

doi:10.1016/j.eswa.2015.09.053

Cited by 18 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three conventional kernels (see ) were used as the RBF, HPOL, and IPOL to find the best kernel function. There are many methods to obtain the optimal parameters of the kernel, such as BBO, 52,53 the artificial bee colony, 54,55 the firefly algorithm, 56,57 PSO, 58 or the genetic algorithm. 59,60 In this study, we used the grid-searching method due to its simplification.…”

Section: Classification Methodsmentioning

confidence: 99%

Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

et al. 2016

View full text Add to dashboard Cite

Finding an appropriate and accurate technology for early detection of disease is significantly important to research early treatments. We proposed some novel automatic classification systems based on the stationary wavelet transform (SWT) and the improved support vector machine (SVM). Magnetic Resonance Imaging (MRI) is commonly used for brain imaging as a non-invasive diagnostic tool to assist the pre-clinical diagnosis. However, MRI generates a large information set, which poses a challenge for classification. To deal with this problem we proposed a new approach, which combines SWT and Principal Component Analysis for feature extraction. In our experiments, three different datasets and four kinds of classifiers of the SVM were employed. The results over 5×6-fold stratified cross-validation (SCV) for Dataset-66, and 5×5-fold SCV for the other two datasets show that the average accuracy is almost 100.00%.

show abstract

Section: Classification Methodsmentioning

confidence: 99%

Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Inspired by the group emergent behaviors in natural systems, swarm intelligence algorithms have gained great interest from interdisciplinary researchers, thus developing a number of swarm-based algorithm recently (Jadon et al 2017;Qin et al 2017;Chu et al 2016b;Hong and Ahn 2016;De et al 2016). Simulating birds flocking and adjusting velocity among each other, particle swarm optimization (PSO) was proposed for numerical optimization (Kennedy and Eberhart 1995;Chu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

An efficient auction mechanism for regional logistics synchronization

Chu

Cai

et al. 2018

J Intell Manuf

View full text Add to dashboard Cite

This paper is the first attempt to propose an efficient auction mechanism for the regional logistics synchronization (RLS) problem, which aims to capture both logistics punctuality and simultaneity in a regional logistics network. The main motivation of RLS is motivated by our industrial collaborator, i.e. a third-party logistics (3PL) company, that if the delay has already occurred or will occur, the customers tend to pursue the simultaneity. We develop the one-sided Vickrey-Clarke-Groves (O-VCG) auction to realize incentive compatibility (on the buy side), budget balance, and individual rationality. The vehicle routing problem faced by the 3PL company is formulated as the lane covering problem with RLS requirements. Given the complexity of the proposed model, three canonical swarm intelligence meta-heuristics are employed to address the auction-based RLS problem. Besides, a superior tracking artificial bee colony with novel information learning mechanism is further developed to explore better solutions. Comparison results reveal the effectiveness of the proposed optimizers in terms of realized social welfare. Experimental results show that the O-VCG auction can achieve high synchronization level, approximately allocative efficiency and (ex post) budget balance.

show abstract