Opposition-Based Multi-Tiered Grey Wolf Optimizer for Stochastic Global Optimization Paradigms

Abstract

Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated, involving divergence and adoption, investigation and utilization, and dissemination techniques. Grey Wolf Optimizer (GWO), a relatively recent algorithm influenced by the dominance structure and poaching deportment of grey wolves, is a very popular technique for solving realistic mechanical and optical technical challenges. Half of the recurrence in the GWO are committed to the exploration and the other half to exploitation, ignoring the importance of maintaining the correct equilibrium to ensure a precise estimate of the global optimum. To address this flaw, a Multi-tiered GWO (MGWO) is formulated, that further accomplishes an appropriate equivalence among exploration and exploitation, resulting in optimal algorithm efficiency. In comparison to familiar optimization methods, simulations relying on benchmark functions exhibit the efficacy, performance, and stabilization of MGWO.

Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

“…Step 2: Run the MSGWO, calculate the SNR according to Equation (22), then update the individual position, iterate to the maximum number of iterations, and finally terminate the iteration. Step 1: Input noisy signals and initialize MSGWO parameters.…”

“…Therefore, it is of great research value to improve the basic grey wolf algorithm and improve its optimization performance [ 21 ]. Vasudha et al proposed a multi-layer grey wolf optimization algorithm to further achieve an appropriate equivalence between exploration and development, thereby improving the efficiency of the algorithm [ 22 ]. Rajput et al proposed an FH model based on the sparsity grey wolf optimization algorithm, which helps to minimize the computational overhead and improve the computational accuracy of the algorithm [ 23 ].…”

Probabilistic Multi-Tiered Grey Wolf Optimizer-Based Routing for Sustainable Sensor Networks

“…Bahl and Kumar (2021) recommended LEACH-Distance Degree Residual Index (DDRI-L) for a more comprehensive search (exploration), in which changing the stage process factor leads to a global refinement search, improving exploration performance. For a confined search (exploitation), Bahl and Bhola (2022) suggested Multi-Tiered Grey Wolf Optimization (MGWO), with the factor coefficient vectors being necessary to emphasize exploitation. Further, the integrated proposed PMR-GWO maintains the exploration-exploitation tradeoff, extends the network's lifetime, improves energy efficiency, and improves the network's total throughput performance.…”