Feature fatigue analysis of product usability using Hybrid ant colony optimization with artificial bee colony approach

2017 ◽  
Vol 76 (6) ◽  
pp. 3999-4016 ◽  
Author(s):  
J. Midhunchakkaravarthy ◽  
S. SelvaBrunda
Keyword(s):  
Ant Colony Optimization ◽  
Artificial Bee Colony ◽  
Fatigue Analysis ◽  
Ant Colony ◽  
Bee Colony ◽  
Feature Fatigue

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.

Modeling of fractional order chaotic systems using artificial bee colony optimization and ant colony optimization

2018 ◽  
Vol 35 (5) ◽  
pp. 5337-5344 ◽  
Author(s):  
Sangeeta Gupta ◽  
Varun Upadhyaya ◽  
Ayush Singh ◽  
Pragya Varshney ◽  
Smriti Srivastava
Keyword(s):  
Ant Colony Optimization ◽  
Fractional Order ◽  
Artificial Bee Colony ◽  
Chaotic Systems ◽  
Ant Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Test scheduling of System-on-Chip using Dragonfly and Ant Lion optimization algorithms

2020 ◽  
pp. 1-13
Author(s):  
Gokul Chandrasekaran ◽  
P.R. Karthikeyan ◽  
Neelam Sanjeev Kumar ◽  
Vanchinathan Kumarasamy
Keyword(s):  
Ant Colony Optimization ◽  
Artificial Bee Colony ◽  
Optimization Algorithms ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
On Chip

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

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