An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

Abstract

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.

The Development and Application Research of Grey Wolf Optimization Algorithm

“…In 2020, Yue et al [14] proposed a fusion of the development capability of GWO and the exploration capability of the Fireworks Algorithm to achieve enhanced global optimization. In 2021, Lan et al [15] introduced an improved Grey Wolf Optimizer based on the utilization of chaos theory and the strengths of the Ocean Predator Algorithm. This enhancement aims to overcome the issue of low convergence accuracy during the optimization process.…”

Dexterity enhancement of continuum robot for natural orifice transluminal endoscopic surgery in the dual‐manipulator collaborative space

“…The Gray Wolf Optimization (GWO) proposed by Mirjalili in 2014 has attracted much attention due to its easy implementation and few parameters 25 . However, it also has the disadvantage of slow convergence and the tendency to fall into local optima 26 . Based on GWO, Sharma et al.…”

“…25 However, it also has the disadvantage of slow convergence and the tendency to fall into local optima. 26 Based on GWO, Sharma et al…”