An Improved Grey Wolf Optimization Algorithm and its Application in Path Planning

• Published in: IEEE access Vol. 9; pp. 121944 - 121956
• Main Authors: Liu, Jingyi, Wei, Xiuxi, Huang, Huajuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convergence; disturbance factors; dynamic weights; Grey wolf algorithm; Heuristic algorithms; lion optimizer algorithm; Mathematical model; Optimization; Optimization algorithms; Particle swarm optimization; Path planning; Statistics; Swarm intelligence
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3108973

Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimization algorithm(IGWO) is proposed. The lion optimizer algorithm and dynamic weights are integrated into the original grey wolf optimization algorithm. When the positions of <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> wolf, <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> wolf, and <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula> wolf are updated, the lion optimizer algorithm is used to add disturbance factors to the wolves to give <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> wolf, <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> wolf, and <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula> wolf active search capabilities. Dynamic weights are added to the grey wolf position update to prevent wolves from losing diversity and falling into local optimum. Through multiple benchmark function test experiments and path planning experiments, the experimental results show that the improved grey wolf optimization algorithm can effectively improve the accuracy and convergence speed, and the optimization effect is better.