scholarly journals Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Kamran Chapi ◽  
Nhat-Duc Hoang ◽  
...  
Keyword(s):  
Spatial Modeling ◽  
Imperialist Competitive Algorithm ◽  
Integrated Approach ◽  
Relevance Vector Machine ◽  
Shallow Landslides ◽  
Competitive Algorithm

The authors wish to make the following corrections to this paper [...]

scholarly journals A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

2018 ◽  
Vol 10 (10) ◽  
pp. 1538 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Chapi ◽  
Nhat-Duc Hoang ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Imperialist Competitive Algorithm ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Predictive Capability ◽  
Competitive Algorithm ◽  
Susceptibility Modeling ◽  
Gis Database ◽  
Proposed Model ◽  
Soft Computing Techniques

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.

MOICA: A novel multi-objective approach based on imperialist competitive algorithm

2013 ◽  
Vol 219 (17) ◽  
pp. 8829-8841 ◽  
Author(s):  
Rasul Enayatifar ◽  
Moslem Yousefi ◽  
Abdul Hanan Abdullah ◽  
Amer Nordin Darus
Keyword(s):  
Imperialist Competitive Algorithm ◽  
Multi Objective ◽  
Competitive Algorithm

Optimization of Cold-Formed Channel Sections Using Imperialist Competitive Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 493-496
Author(s):  
Roya Kohandel ◽  
Behzad Abdi ◽  
Poi Ngian Shek ◽  
M.Md. Tahir ◽  
Ahmad Beng Hong Kueh
Keyword(s):  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Computational Method ◽  
Compression Force ◽  
Sqp Method ◽  
Competitive Algorithm ◽  
Different Types ◽  
Formed Channel ◽  
Good Agreement

The Imperialist Competitive Algorithm (ICA) is a novel computational method based on the concept of socio-political motivated strategy, which is usually used to solve different types of optimization problems. This paper presents the optimization of cold-formed channel section subjected to axial compression force utilizing the ICA method. The results are then compared to the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) algorithm for validation purpose. The results obtained from the ICA method is in good agreement with the GA and SQP method in terms of weight but slightly different in the geometry shape.

Imperialist Competitive Algorithm for Multiobjective Optimization of Ellipsoidal Head of Pressure Vessel

2011 ◽  
Vol 110-116 ◽  
pp. 3422-3428 ◽  
Author(s):  
Behzad Abdi ◽  
Hamid Mozafari ◽  
Ayob Amran ◽  
Roya Kohandel
Keyword(s):  
Genetic Algorithm ◽  
Pressure Vessel ◽  
Shear Force ◽  
Minimum Weight ◽  
Bending Moment ◽  
Imperialist Competitive Algorithm ◽  
Optimum Shape ◽  
Working Pressure ◽  
Competitive Algorithm ◽  
Bending Stresses

This work devoted to an ellipsoidal head of pressure vessel under internal pressure load. The analysis is aimed at finding an optimum weight of ellipsoidal head of pressure vessel due to maximum working pressure that ensures its full charge with stresses by using imperialist competitive algorithm and genetic algorithm. In head of pressure vessel the region of its joint with the cylindrical shell is loaded with shear force and bending moments. The load causes high bending stresses in the region of the joint. Therefore, imperialist competitive algorithm was used here to find the optimum shape of a head with minimum weight and maximum working pressure which the shear force and the bending moment moved toward zero. Two different size ellipsoidal head examples are selected and studied. The imperialist competitive algorithm results are compared with the genetic algorithm results.

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