The whale optimization algorithm is based on the bubble-net attacking behavior of humpback whales and simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. However, the basic whale optimization algorithm has the disadvantage of search stagnation, easily falls into a local optimum, has slow convergence speed and has low calculation accuracy. The Lévy flight strategy is beneficial for expanding the search range and prevents the algorithm from falling into a local optimum, which enhances the global search ability. The ranking-based mutation operator can increase the selection probability and accelerate the convergence speed to enhance the local search ability. To overcome these shortcomings and avoid premature convergence, the Lévy flight strategy and the ranking-based mutation operator are added to the whale optimization algorithm. In this paper, an enhanced whale optimization algorithm is proposed, which realizes complementary advantages to balance exploration and exploitation. Eighteen benchmark test functions and five structural engineering design problems are used to verify the robustness and overall optimization performance of the enhanced whale optimization algorithm. The experimental results show that the enhanced whale optimization algorithm is an effective and feasible method that has a fast convergence speed, high calculation accuracy, strong robustness and stability.

鲸鱼优化算法基于座头鲸的泡泡网攻击行为，并模拟了围绕猎物，泡泡网攻击和寻找猎物以获得全局最优解。然而，基本的鲸鱼优化算法具有搜索停滞的缺点，容易陷入局部最优，收敛速度慢并且计算精度低。Lévy飞行策略有利于扩大搜索范围，并防止算法陷入局部最优状态，从而增强了全局搜索能力。基于排序的变异算子可以提高选择概率，加快收敛速度​​，提高局部搜索能力。为了克服这些缺点并避免过早收敛，鲸鱼优化算法中增加了Lévy飞行策略和基于排名的变异算子。提出了一种改进的鲸鱼优化算法，实现了平衡勘探与开发的互补优势。18个基准测试功能和5个结构工程设计问题用于验证增强型鲸鱼优化算法的鲁棒性和整体优化性能。实验结果表明，改进的鲸鱼优化算法是一种收敛速度快，计算精度高，鲁棒性和稳定性强的有效可行的方法。18个基准测试功能和5个结构工程设计问题用于验证增强型鲸鱼优化算法的鲁棒性和整体优化性能。实验结果表明，改进的鲸鱼优化算法是一种收敛速度快，计算精度高，鲁棒性和稳定性强的有效可行的方法。18个基准测试功能和5个结构工程设计问题用于验证增强型鲸鱼优化算法的鲁棒性和整体优化性能。实验结果表明，改进的鲸鱼优化算法是一种收敛速度快，计算精度高，鲁棒性和稳定性强的有效可行的方法。