Citation: Hou, Y.; Gao, H.; Wang, Z.;
Du, C. Improved Grey Wolf
Optimization Algorithm and
Application. Sensors 2022, 22, 3810.
https://doi.org/10.3390/s22103810
Academic Editors: Luis Payá, Oscar
Reinoso García and Helder Jesus
Araújo
Received: 27 April 2022
Accepted: 16 May 2022
Published: 17 May 2022
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Article
Improved Grey Wolf Optimization Algorithm and Application
Yuxiang Hou
1,2
, Huanbing Gao
1,2,
*, Zijian Wang
1,2
and Chuansheng Du
1,2
1
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China;
2020080118@stu.sdjzu.edu.cn (Y.H.); zijian1632022@163.com (Z.W.); chuansheng2022@163.com (C.D.)
2
Shandong Key Laboratory of Intelligent Building Technology, Jinan 250101, China
* Correspondence: gaohuanbing2004@sdjzu.edu.cn
Abstract:
This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of
instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong
optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping
to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor
based on the Gaussian distribution change curve to balance the global and local searchability. In
addition, an improved dynamic proportional weighting strategy is proposed that can update the
positions of grey wolves so that the convergence of this algorithm can be accelerated. The proposed
improved GWO algorithm results are compared with the other eight algorithms through several
benchmark function test experiments and path planning experiments. The experimental results show
that the improved GWO has higher accuracy and faster convergence speed.
Keywords: Grey Wolf Optimizer; tent mapping; convergence factor; path planning
1. Introduction
Path planning is widely used in mobile robot navigation, which of the aim is to find
an optimal trajectory that connects the starting point with the target point while avoiding
collisions with obstacles [
1
,
2
]. There are many commonly used algorithms, such as A*
algorithm [
3
], particle swarm algorithm (PSO) [
4
,
5
], genetic algorithm (GA) [
6
], and grey
wolf algorithm (GWO) [7–9].
GWO is a new pack intelligence optimization algorithm that is widely used in many
significant fields. It mainly imitates the grey wolf race pack’s hierarchical pattern and hunt-
ing behavior and achieves optimization through the wolf pack’s tracking, encircling, and
pouncing behaviors. Compared with traditional optimization algorithms such as PSO and
GA, GWO has the advantages of fewer parameters, simple principles, and implementing
easily. However, GWO has the disadvantages of slow convergence speed, low solution
accuracy, and easy to fall into the local optimum. For this reason, many scholars have made
many improvements. Yang Zhang [
10
] proposed MGWO, which introduced an exponential
regular convergence factor strategy, an adaptive update strategy, and a dynamic weighting
strategy to improve the GWO search capability. Min Wang [
11
] proposed NGWO, which
used reverse learning of the initial racial group and introduced a nonlinear convergence
factor to improve the algorithm search capability. Luis Rodriguez [
12
] proposed the Grey
Wolf algorithm (GWO-fuzzy) based on a fuzzy hierarchical operator and compared two
proportional weighting strategies. Saremi [
13
] proposed the grey Wolf Algorithm for Evolu-
tionary Population Dynamics (GWO-EPD), which focuses on the location change of poorly
adapted grey wolf individuals to improve search accuracy. Qiuping Wang [
14
] proposed an
improved grey wolf algorithm (CGWO), which uses the cosine law to vary the convergence
factor to improve the searchability, and introduces a proportional weight based on the
step Euclidean distance to update the position of the grey wolf to speed up the conver-
gence speed. Shipeng Wang [
15
] proposed a new hybrid algorithm (FWGWO), which
combines the advantages of both algorithms and effectively achieves the global optimum.
Sensors 2022, 22, 3810. https://doi.org/10.3390/s22103810 https://www.mdpi.com/journal/sensors
