Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior ofE. colibacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
In this study, we investigate multi-scale features extracted from baseline structural magnetic resonance imaging (MRI) for classifying patients with mild cognitive impairment (MCI), who have either converted or not converted to Alzheimer's disease (AD) three years after their baseline visit. Total 549 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) database are included, and there are 228 Normal controls (NC), 133 MCI patients (71 of them converted to AD within 3 years, referred as MCI converters, or MCIc) and 188 AD patients. The images are preprocessed with the standard voxel-based morphometry method with segmentation of grey matter, white matter and cerebrospinal fluid. Wavelet frame, a multi-scale image representation approach, is applied to extract features of different scales and directions from the processed grey matter image data. The features are extracted for both whole grey matter images and grey matter images of the hippocampus region. The support vector machine is adopted to construct classifiers for MCIc and MCI non-converters (MCInc). The accuracy using a leave-one-out procedure for classification of AD vs NC and MCIc vs MCInc is 84.13% and 76.69% respectively, both achieved by local hippocampus data. Our study shows that the proposed multi-scale method is capable of discriminating MCI converters and non-converters, and it can be potentially useful for MCI prognosis in clinical applications.
Summary
Deformation is the most intuitive reflection of comprehensive behavior of concrete dams; it is of great significance to predict and interpret the deformation observation data for dam health monitoring. The world's highest concrete dam, Jinping I arch dam in China, was discussed in this paper. Aiming at its annually measured continuous growth phenomenon of dam body deformation towards the downstream direction when reservoir keeps stable at the normal water level of 1,880.0 m, influences of cement hydration heat‐induced temperature rise effect, valley contraction, and dam material creep on deformation behavior of this dam were estimated by finite element method (FEM) and the measured data. Combined with the results of the hydraulic, seasonal, and time (HST) model, the abnormal deformation behavior was detected to be jointly caused by the hysteretic hydraulic deformation and the ambient temperature drop effect. Subsequently, to solve the deficiency that the traditional HST model cannot reasonably explain this measured deformation behavior, a hysteretic hydraulic component was introduced into the HST model, and a special hydraulic, hysteretic, seasonal, and time (HHST) model was proposed. Based on the numerical simulation of viscoelastic FEM and the constrained least square method, the newly added component was represented by a continuous piecewise fitting function, with model factors of previous relative water depth and cumulative days of the current water level stage. HHST model results of Jinping I arch dam show that the measured abnormal displacement increment of dam body is 70% caused by the ambient temperature drop effect and 30% caused by the viscoelastic hysteretic hydraulic deformation.
Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior ofE. colibacteria. This paper presents a variation on the original BFO algorithm, namely, the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improve the original BFO in solving complex optimization problems. This significant improvement is achieved by applying two cooperative approaches to the original BFO, namely, the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. The experiments compare the performance of two CBFO variants with the original BFO, the standard PSO and a real-coded GA on four widely used benchmark functions. The new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
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