Visible to the public Biblio

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2020-12-14
Gu, Y., Liu, N..  2020.  An Adaptive Grey Wolf Algorithm Based on Population System and Bacterial Foraging Algorithm. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :744–748.
In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.
2017-09-15
Singh, Gagandeep, Kad, Sandeep.  2016.  Comparative Study of Watermarking an Image Using GA and BFO with GA and HBO Technique. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. :5:1–5:5.

Multimedia security and copyright protection has been a popular topic for research and application, due to the explosion of data exchange over the internet and the widespread use of digital media. Watermarking is a process of hiding the digital information inside a digital media. Information hiding as digital watermarks in multimedia enables protection mechanism in decrypted contents. This paper presents a comparative study of existing technique used for digital watermarking an image using Genetic Algorithm and Bacterial Foraging Algorithm (BFO) based optimization technique with proposed one which consists of Genetic Algorithm and Honey Bee based optimization technique. The results obtained after experiment conclude that, new method has indeed outperformed then the conventional technique. The implementation is done over the MATLAB.