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Majhi, D., Rao, M., Sahoo, S., Dash, S. P., Mohapatra, D. P..  2020.  Modified Grey Wolf Optimization(GWO) based Accident Deterrence in Internet of Things (IoT) enabled Mining Industry. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). :1–4.
The occurrences of accidents in mining industries owing to the fragile health conditions of mine workers are reportedly increasing. Health conditions measured as heart rate or pulse, glycemic index, and blood pressure are often crucial parameters that lead to failure in proper reasoning when not within acceptable ranges. These parameters, such as heartbeat rate can be measured continuously using sensors. The data can be monitored remotely and, when found to be of concern, can send necessary alarms to the mine manager. The early alarm notification enables the mine manager with better preparedness for managing the reach of first aid to the accident spot and thereby reduce mine fatalities drastically. This paper presents a framework for deterring accidents in mines with the help of the Grey Wolf Optimization approach.
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.