Improved grey wolf optimizer based on opposition and quasi learning approaches for optimization: case study autonomous vehicle including vision system

2022 ◽  
Author(s):  
M. Elsisi
Keyword(s):  
Vision System ◽  
Autonomous Vehicle ◽  
Grey Wolf Optimizer ◽  
Learning Approaches ◽  
Grey Wolf ◽  
Improved Grey Wolf Optimizer

An improved grey wolf optimizer for solving engineering problems

2021 ◽  
Vol 166 ◽  
pp. 113917 ◽  
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Shokooh Taghian ◽  
Seyedali Mirjalili
Keyword(s):  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Engineering Problems ◽  
Improved Grey Wolf Optimizer

scholarly journals An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3178
Author(s):  
Pu Lan ◽  
Kewen Xia ◽  
Yongke Pan ◽  
Shurui Fan
Keyword(s):  
Chaos Theory ◽  
Prediction Accuracy ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Practical Applications ◽  
Marine Predator ◽  
Search Speed ◽  
Improved Grey Wolf Optimizer ◽  
Better Than

In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.

Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm

2021 ◽  
Vol 146 (1-2) ◽  
pp. 833-849
Author(s):  
Ali Kozekalani Sales ◽  
Enes Gul ◽  
Mir Jafar Sadegh Safari ◽  
Hadi Ghodrat Gharehbagh ◽  
Babak Vaheddoost
Keyword(s):  
Extreme Learning Machine ◽  
Water Depth ◽  
Lake Water ◽  
Hybrid Algorithm ◽  
Urmia Lake ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Learning Machine ◽  
Improved Grey Wolf Optimizer
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