Abstract In recent years, a range of novel and pseudonovel optimization algorithms has been proposed for solving engineering problems. Swarm intelligence optimization algorithms (SIAs) have become popular methods, and the whale optimization algorithm (WOA) is one of the highly discussed SIAs. However, regardless of novelty concerns about this method, the basic WOA is a weak method compared to top differential evolutions and particle swarm variants, and it suffers from the problem of poor initial population quality and slow convergence speed. Accordingly, in this paper, to increase the diversity of WOA versions and enhance the performance of WOA, a new WOA variant, named LXMWOA, is proposed, and based on the Lévy initialization strategy, the directional crossover mechanism, and the directional mutation mechanism. Specifically, the introduction of the Lévy initialization strategy allows initial populations to be dynamically distributed in the search space and enhances the global search capability of the WOA. Meanwhile, the directional crossover mechanism and the directional mutation mechanism can improve the local exploitation capability of the WOA. To evaluate its performance, using a series of functions and three models of engineering optimization problems, the LXMWOA was compared with a broad array of competitive optimizers. The experimental results demonstrate that the LXMWOA is significantly superior to its exploration and exploitation capability peers. Therefore, the proposed LXMWOA has great potential to be used for solving engineering problems.

中文翻译：

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鲸鱼算法不成熟性能的定向变异与交叉应用于工程优化

摘要 近年来，人们提出了一系列新的和伪新的优化算法来解决工程问题。群体智能优化算法 (SIA) 已成为流行的方法，而鲸鱼优化算法 (WOA) 是备受关注的 SIA 之一。然而，不管这种方法的新颖性如何，与顶微分进化和粒子群变体相比，基本WOA是一种弱方法，并且存在初始种群质量差和收敛速度慢的问题。因此，在本文中，为了增加 WOA 版本的多样性并提高 WOA 的性能，提出了一种新的 WOA 变体，称为 LXMWOA，它基于 Lévy 初始化策略、方向交叉机制和方向变异机制。具体来说，Lévy初始化策略的引入使得初始种群在搜索空间中动态分布，增强了WOA的全局搜索能力。同时，定向交叉机制和定向变异机制可以提高WOA的局部开发能力。为了评估其性能，使用一系列函数和三个工程优化问题模型，将 LXMWOA 与广泛的竞争优化器进行了比较。实验结果表明，LXMWOA 显着优于其勘探和开发能力同行。因此，提出的 LXMWOA 在解决工程问题方面具有很大的潜力。