Department of Control Science and Engineering, Jilin University, Changchun 130022, China.
Comput Intell Neurosci. 2022 Jan 30;2022:3603607. doi: 10.1155/2022/3603607. eCollection 2022.
Grey wolf optimizer (GWO) is an up-to-date nature-inspired optimization algorithm which has been used for solving many of the real-world applications since it was proposed. In the standard GWO, individuals are guided by the three dominant wolves alpha, beta, and delta in the leading hierarchy of the swarm. These three wolves provide their information about the potential locations of the global optimum in the search space. This learning mechanism is easy to implement. However, when the three wolves are in conflicting directions, an individual may not obtain better knowledge to update its position. To improve the utilization of the population knowledge, in this paper, we proposed a grey wolf optimizer based on the dimensional learning strategy (DLGWO). In the DLGWO, the three dominant wolves construct an exemplar wolf through the dimensional learning strategy (DLS) to guide the grey wolves in the swarm. Thereafter, to reinforce the exploration ability of the algorithm, the Levy flight is also utilized in the proposed method. 23 classic benchmark functions and engineering problems are used to test the effectiveness of the proposed method against the standard GWO, variants of the GWO, and other metaheuristic algorithms. The experimental results show that the proposed DLGWO has good performance in solving the global optimization problems.
灰狼优化器（GWO）是一种最新的基于自然启发的优化算法，自提出以来，已被用于解决许多实际问题。在标准 GWO 中，个体由群体中领导层次的三个主导狼阿尔法、贝塔和德尔塔引导。这三只狼提供了它们在搜索空间中关于全局最优位置的信息。这种学习机制易于实现。然而，当三只狼处于冲突的方向时，个体可能无法获得更好的知识来更新其位置。为了提高种群知识的利用率，本文提出了一种基于维度学习策略（DLS）的灰狼优化器（DLGWO）。在 DLGWO 中，三只主导狼通过维度学习策略（DLS）构建一个范例狼来引导群体中的灰狼。此后，为了增强算法的探索能力，还在提出的方法中利用了莱维飞行。使用 23 个经典基准函数和工程问题来测试所提出的方法对标准 GWO、GWO 的变体和其他元启发式算法的有效性。实验结果表明，所提出的 DLGWO 在解决全局优化问题方面具有良好的性能。
