scholarly journals A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and Improved Artificial Bee Colony Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 117612-117626 ◽  
Author(s):  
Jingpeng Gao ◽  
Yi Lu ◽  
Junwei Qi ◽  
Liangxi Shen
Keyword(s):  
Matrix Factorization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Recognition System ◽  
Radar Signal ◽  
Signal Recognition ◽  
Bee Colony ◽  
Non Negative Matrix Factorization

scholarly journals Hancitor malware recognition using swarm intelligent technique

2020 ◽  
Vol 2 (3) ◽  
pp. 103-112
Author(s):  
Laheeb M. Ibrahim ◽  
Maisirreem Atheeed Kamal ◽  
AbdulSattar A. Al-Alusi
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Early Recognition ◽  
Computer Systems ◽  
Recognition System ◽  
Gray Wolf ◽  
Global Risk ◽  
Bee Colony ◽  
Intelligent Technique ◽  
Simple Rules

Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm Intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the Gray Wolf Optimization algorithm (GWO) and Artificial Bee Colony algorithm (ABC), which can effectively recognize Hancitor in networks.

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

Artificial bee colony algorithm with modified search strategy

2013 ◽  
Vol 32 (12) ◽  
pp. 3326-3330
Author(s):  
Yin-xue ZHANG ◽  
Xue-min TIAN ◽  
Yu-ping CAO
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Bee Colony
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