An Optimal Differential Vector Incorporated Whale Optimizer
An Optimal Differential Vector Incorporated Whale Optimizer
复制标题
DOI：
10.1109/iscid56505.2022.00009
复制
发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Jiatianyi Yu;Haichuan Yang;Baohang Zhang;Shibo Dong;Shangce Gao
中科院分区：
文献类型：
--
作者：
Jiatianyi Yu;Haichuan Yang;Baohang Zhang;Shibo Dong;Shangce Gao
鲸鱼优化算法（WOA）是一种比较经典的优化算法，它源于鲸鱼的捕猎行为。虽然WOA非常适合开采，但这也导致勘探能力水平较低，难以突破局部最优。在这项研究中，通过将最佳的差分向量在一个有效的鲸鱼优化器，我们提出了一个差分扰动操作，打破了局部最优，即差分向量鲸鱼优化算法（DVWOA）。基于IEEE CEC2017基准函数的实验结果表明，DVWOA算法在全局搜索、收敛速度和种群多样性等方面都有较好的性能。
The whale optimization algorithm (WOA) is one of the more classic optimization algorithms, which derived from the hunting actions of whale. While WOA is well suited to exploitation, this also leads to a low level of exploration capability, making it difficult to break out of the local optimum. In this study, by incorporating the optimal differential vector in an effective whale optimizer, we propose a differential perturbation operation to break out of the local optimum namely differential vector whale optimization algorithm (DVWOA). According to experimental results based on IEEE CEC2017 benchmark functions, DVWOA is beneficial in the aspects of global search, convergence speed, and population diversity.