文档介绍：电子测量技术
Electronic Measurement Technology
ISSN 1002-7300,CN 11-2175/TN
hm
Xu Le Zhao Wenlong
(School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China)
Abstract: For the sake of settle the disputes of algorithm poor optimization performance and slow training speed of gray wolf
optimization algorithm, a new gray wolf optimization algorithm is proposed. In the initialization part of the algorithm, the reverse learning
strategy is used to generate ordered gray wolf individuals, which validly ameliorates the convergence speed of the algorithm. The search
ability of the algorithm is coordinated by designing a new nonlinear convergence factor and optimizing the individual location update
strategy to reduce the probability of falling into local optimization. The elite selection retention strategy and tournament selection strategy
are introduced to accelerate the population evolution and improve the convergence speed of the algorithm. The basic function test results
and track planning simulation experiment verify that the new gray wolf optimization algorithm has strong astringency and high
optimization accuracy, and th