Ant Colony Estimator: An intelligent particle filter based on

Heris, S. Mostapha Kalami; Khaloozadeh, Hamid

doi:10.1016/j.engappai.2013.11.005

Cited by 29 publications

(12 citation statements)

References 37 publications

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“…In features selection, the ACO tries to find the best solutions using prior information from previous iterations. The search for the optimal feature subset consists of an ant traveling through the graph with a minimum number of nodes required for satisfaction of stopping criterion 86 . For further information refer to supplementary Methods section.…”

Section: Dimension Reduction Methodsmentioning

confidence: 99%

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Zamani

Sadr

Javadi

2021

Preprint

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Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

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Section: Dimension Reduction Methodsmentioning

confidence: 99%

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Zamani

Sadr

Javadi

2021

Preprint

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“…In order to find the solution vector of x, it is necessary to minimize the objective function (OF). The steps below express the computations in the ACO algorithm [101][102][103]:…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks

et al. 2020

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Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on handling asphaltenes deposition are still lacking. In this study, three efficient artificial intelligent models including group method of data handling (GMDH), least squares support vector machine (LSSVM), and artificial neural network (ANN) are proposed for estimating asphaltenes adsorption onto NiO/SAPO-5, NiO/ZSM-5, and NiO/AlPO-5 nanocomposites based on a databank of 252 points. Variables influencing asphaltenes adsorption include pH, temperature, amount of nanocomposites over asphaltenes initial concentration (D/C0), and nanocomposites characteristics such as BET surface area and volume of micropores. The models are also optimized using nine optimization techniques, namely coupled simulated annealing (CSA), genetic algorithm (GA), Bayesian regularization (BR), scaled conjugate gradient (SCG), ant colony optimization (ACO), Levenberg–Marquardt (LM), imperialistic competitive algorithm (ICA), conjugate gradient with Fletcher-Reeves updates (CGF), and particle swarm optimization (PSO). According to the statistical analysis, the proposed RBF-ACO and LSSVM-CSA are the most accurate approaches that can predict asphaltenes adsorption with average absolute percent relative errors of 0.892% and 0.94%, respectively. The sensitivity analysis shows that temperature has the most impact on asphaltenes adsorption from model oil solutions.

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“…Due to this fact, a Gaussian model was proposed by the construction of a list of probable solutions to generalize this approach in continuous space. Some special solutions exist in solution archives all over time ( Heris & Khaloozadeh, 2014). These solutions can be obtained by optimization algorithm.the minimization of cost function as the first step of the ACO approach starts with finding the vector x ∈ X ⊆ R n x by the below steps (Lozano et al, 2006):…”

Section: Acomentioning

confidence: 99%

Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms

Bemani

Baghban

Mosavi

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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One of the important parameters illustrating the mass transfer process is the diffusion coefficient of carbon dioxide which has a great impact on carbon dioxide storage in marine ecosystems, saline aquifers, and depleted reservoirs. Due to the complex interpretation approaches and special laboratory equipment for measurement of carbon dioxide-brine system diffusivity, the computational and mathematical methods are preferred. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is coupled with five different evolutionary algorithms for predicting the diffusivity coefficient of carbon dioxide. The R 2 values forthe testing phase are 0.9978, 0.9932, 0.9854, 0.9738 and 0.9514 for ANFIS optimized by particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), backpropagation (BP), and differential evolution (DE), respectively. The hybrid machine learning model of ANFIS-PSO outperforms the other models.

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Ant Colony Estimator: An intelligent particle filter based on

Cited by 29 publications

References 37 publications

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks

Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms

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