School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China.
Sensors (Basel). 2022 Sep 9;22(18):6843. doi: 10.3390/s22186843.
Grey wolf optimization (GWO) is a meta-heuristic algorithm inspired by the hierarchy and hunting behavior of grey wolves. GWO has the superiorities of simpler concept and fewer adjustment parameters, and has been widely used in different fields. However, there are some disadvantages in avoiding prematurity and falling into local optimum. This paper presents an improved grey wolf optimization (IGWO) to ameliorate these drawbacks. Firstly, a modified position update mechanism for pursuing high quality solutions is developed. By designing an ameliorative position update formula, a proper balance between the exploration and exploitation is achieved. Moreover, the leadership hierarchy is strengthened by proposing adaptive weights of , and . Then, a dynamic local optimum escape strategy is proposed to reinforce the ability of the algorithm to escape from the local stagnations. Finally, some individuals are repositioned with the aid of the positions of the leaders. These individuals are pulled to new positions near the leaders, helping to accelerate the convergence of the algorithm. To verify the effectiveness of IGWO, a series of contrast experiments are conducted. On the one hand, IGWO is compared with some state-of-the-art GWO variants and several promising meta-heuristic algorithms on 20 benchmark functions. Experimental results indicate that IGWO performs better than other competitors. On the other hand, the applicability of IGWO is verified by a robot global path planning problem, and simulation results demonstrate that IGWO can plan shorter and safer paths. Therefore, IGWO is successfully applied to the path planning as a new method.
灰狼优化（GWO）是一种启发式算法，灵感来自灰狼的等级制度和狩猎行为。GWO 具有概念简单、调整参数少的优点，已广泛应用于不同领域。但是，它在避免早熟和陷入局部最优方面存在一些缺点。本文提出了一种改进的灰狼优化（IGWO）来改善这些缺点。首先，设计了一种改进的位置更新机制，以追求高质量的解决方案。通过设计改进的位置更新公式，在探索和利用之间实现了适当的平衡。此外，通过提出自适应权重、和 ，增强了领导层的等级制度。然后，提出了一种动态局部最优逃逸策略，以增强算法逃离局部停滞的能力。最后，借助领导者的位置重新定位一些个体。这些个体被拉到领导者附近的新位置，有助于加速算法的收敛。为了验证 IGWO 的有效性，进行了一系列对比实验。一方面，IGWO 与一些最先进的 GWO 变体和几种有前途的元启发式算法在 20 个基准函数上进行了比较。实验结果表明，IGWO 比其他竞争对手表现更好。另一方面，通过机器人全局路径规划问题验证了 IGWO 的适用性，仿真结果表明 IGWO 可以规划更短、更安全的路径。因此，IGWO 成功地作为一种新方法应用于路径规划。
