Year: 2020
Type: article
Abstract: The grey wolf optimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta‐heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out; and results show its better performance than the standard GWO algorithm and other well‐known meta‐heuristic algorithms
Source: The Journal of Engineering
Authors: Zheng‐ming Gao, Juan Zhao, Yu‐rong Hu
Institution: Jingchu University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,000
Award: G3428039274
OpenAlex ID: W3042162268