scholarly journals Fault Detection Techniques Prioritization using Bee Colony Optimization and then Comparison with Ant Colony Optimization

2013 ◽  
Vol 69 (17) ◽  
pp. 16-20 ◽  
Author(s):  
Mandeep KaurBedi ◽  
Sheena Singh
Keyword(s):  
Fault Detection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Detection Techniques ◽  
Bee Colony ◽  
Bee Colony Optimization

scholarly journals Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony

2021 ◽  
pp. 104-113
Author(s):  
Г.В. Худов ◽  
І.А. Хижняк
Keyword(s):  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Artificial Bee Colony ◽  
Visual Assessment ◽  
Comparative Assessment ◽  
Ant Colony ◽  
Bee Colony ◽  
Segmentation Methods ◽  
Segmentation Quality

The article discusses the methods of swarm intelligence, namely, an improved method based on the ant colony optimization and the method of an artificial bee colony. The goal of the work is to carry out a comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony. Segmentation of tonal optical-electronic images was carried out using the proposed methods of swarm intelligence. The results of the segmentation of optical-electronic images obtained from the spacecraft are presented. A visual assessment of the quality of segmentation results was carried out using improved methods. The classical errors of the first and second kind of segmentation of optoelectronic images are calculated for the proposed methods of swarm intelligence and for known segmentation methods. The features of using each of the proposed methods of swarm intelligence are determined. The tasks for which it is better to use each of the proposed methods of swarm intelligence are determined.

scholarly journals Optimization in Edge Detection using Ant Colony Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8167-8170
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Computation Time ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Detection Techniques ◽  
Medical Security ◽  
Satellite Applications ◽  
Image Edge

Image processing is now emerged in different fields like medical, security and surveillance, remote sensing & satellite applications and much more. Image processing includes different operations such as feature extraction, object detection and recognition, X-ray scanning etc. All such operations required edge detection to get better quality image. Edge detection is performed to distinguish different objects in an image by finding the boundaries or edges between them. Edges are used to isolate particular objects from their background as well as to recognize or classify objects. In this paper, comparison of various edge detection techniques such as Sobel, Prewitt, Roberts, Canny, LoG and Ant Colony Optimization Algorithm is given. Ant colony Optimization(ACO) use parallelism which reduces the computation time as size of an image increases.

scholarly journals A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

2018 ◽  
Vol 14 (10) ◽  
pp. 40
Author(s):  
Beichen Chen
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Cluster Structure ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Ant Colony Optimization Algorithm ◽  
Positioning Accuracy ◽  
Bee Colony ◽  
Positioning Algorithm

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to enhance the positioning accuracy of wireless sensor network (WSN) nodes. For this purpose, a WSN node positioning algorithm was proposed based on artificial bee colony (ABC) algorithm and the neural network (NN). First, the parameters between three anchor nodes and the target node were measured. Then, the ABC and NN were introduced to simulate and predict the ranging error, and the weight was determined according to the results. In the proposed algorithm, the cluster structure was effectively combined with the NN model. The weight of backpropagation NN was optimized by the ant colony optimization (ACO) algorithm. Then, the ACO-optimized NN was used to fuse the data collected by WSN nodes. The simulation results show that the proposed algorithm can improve the positioning accuracy of WSN nodes and reduce the time of the search. The research findings shed new light on the positioning of WSN nodes.</span>

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