A Review of Ant Colony Optimization Based Methods for Detecting Epistatic Interactions

Shang, Junliang; Wang, Xuan; Wu, Xiaoyang; Sun, Yingxia; Qian, Dali; Liu, Jin-Xing; Zhang, Honghai

doi:10.1109/access.2019.2894676

Cited by 31 publications

(18 citation statements)

References 70 publications

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“…In last decade ACO has attracted an increasing number of researchers, and its applications have developed remarkably [33]. ACO was efficiently employed in several remote sensing applications, such as optimal attribute subset selection [14], image segmentation [34], selection of parameters [35], and object derivation [36].…”

Section: Optimization Of the Conditioning Factorsmentioning

confidence: 99%

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

Fanos

Pradhan

2019

IEEE Access

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Rockfall source identification is the most challenging task in rockfall hazard and risk assessment. This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow. The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas. High-resolution light detection and ranging data (LiDAR) was employed to derive the digital terrain model (DTM), from which several conditioning factors were extracted. These conditioning factors were optimized utilizing the ant colony optimization (ACO). Different machine learning algorithms, namely, logistic regression (LR), random tree (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN), in addition to their ensemble models (stacking, bagging, and voting), were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. On the other hand, the Gaussian mixture model (GMM) was optimized and applied for automatically identifying the slope threshold of the occurrence of the different landslide types. In order to reduce the model sensitivity to the alteration in various conditioning factors and to improve the model computations performance, land use probability area was formed. The rockfall sources were identified by integrating the probability maps and the reclassified slope raster based on the GMM results. The accuracy assessment reveals that the developed hybrid model can identify the probable rockfall regions with an accuracy of 0.95 based on the validation dataset and 0.94 on based the training dataset. The slope thresholds calculated by GMM were found to be > 58 • , 22 •-58 • , and 9 •-22 • for rockfall, shallow landslide, and debris flow, respectively. This indicates that the model can be generalized and replicated in different regions, and the proposed method can be applied in various landslides studies.

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Section: Optimization Of the Conditioning Factorsmentioning

confidence: 99%

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

Fanos

Pradhan

2019

IEEE Access

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“…For example, in Figure 3, an ant has reached SNP i ; next; it selects an unreached SNP j with probability τ ij n j=1 τ ij . A detailed ACO algorithm for detecting SNP interactions was introduced by Shang et al [23] and Jing and Shen [24]. Shang et al reviewed the ACO algorithms that are applied to detect epistatic interactions and analyzed the strengths and limitations of the involved ACO methods in detail.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

“…Guan and Zhao proposed a self-adjusting ACO-based information entropy to identify epistatic interactions [32]. Shang et al systematically reviewed 25 ACO-based epistasis interaction approaches [23].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

2019

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Detecting high-order single-nucleotide polymorphism (SNP) interactions is of great importance for the discovery of pathogenic causes of human complex diseases. However, a considerable computing challenge exists in analyzing each SNP combination at a genome-wide scale. Swarm intelligence search (SIS) is an effective and efficient method for solving NP-hard problems and has been extensively researched for detecting high-order SNP interactions. In this review, we first analyze the strengths and limitations of existing methods such as exhaustive search using cluster computing and parallel computing, stochastic search and high-performance computing. Then, SIS algorithms for the detection of high-order SNP interactions are introduced in detail. The algorithms discussed are the genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), particle swarm optimization (PSO), differential evolution (DE), cuckoo search (CS), fish swarm (FS) and artificial bee colony (ABC). Finally, we discuss the characteristics and limitations of the involved methods and provide several suggestions for improving SIS algorithms to detect high-order SNP interactions.

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“…However, it is challenging for all three strategies to find an optimal feature subset using the objective function. Various search meta-heuristics were proposed to resolve this problem, e.g., genetic algorithms (GA) [ 10 , 11 ], particle swarm (PSO) [ 12 ], and ant colony optimization (ACO) [ 13 ]. These techniques have been proven to solve challenging computational problems.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

Guindo

Kabir

Chen

et al. 2021

Sensors

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Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R2) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R2p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R2p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer.

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A Review of Ant Colony Optimization Based Methods for Detecting Epistatic Interactions

Cited by 31 publications

References 70 publications

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR

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