School of Information Engineering, Sanming University, Sanming 365004, China.
School of Education and Music, Sanming University, Sanming 365004, China.
Math Biosci Eng. 2023 Jun 9;20(7):13267-13317. doi: 10.3934/mbe.2023592.
This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.
本文提出了一种改进的白鲸优化（IBWO）算法，主要用于解决全局优化问题和工程问题。这种改进是为了解决探索和开发之间的不平衡问题，以及解决白鲸优化（BWO）的收敛精度和速度不足的问题。在 IBWO 中，我们使用了一种新的群体行为策略（GAS），它取代了 BWO 中的探索阶段。它的灵感来自于白鲸在自然界中的群体狩猎行为。GAS 使个体白鲸保持在一起，使它们能够一起躲避来自天敌虎鲨的威胁。它还允许个体白鲸之间交换位置信息，从而增强局部和全局搜索之间的平衡。在此基础上，添加了动态针孔成像策略（DPIS）和二次插值策略（QIS），以提高 IBWO 的全局优化能力和搜索率，并保持多样性。在对比实验中，通过使用 CEC2017 和 CEC2020 不同维度的基准函数对优化算法（IBWO）的性能进行了测试。通过观察实验数据、收敛曲线和箱线图进行性能分析，并使用 Wilcoxon 秩和检验对结果进行测试。结果表明，IBWO 具有良好的优化性能和鲁棒性。最后，通过五个工程问题验证了 IBWO 在实际工程问题中的适用性。
