Nadimi-Shahraki Mohammad H, Zamani Hoda, Asghari Varzaneh Zahra, Mirjalili Seyedali
Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran.
Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran.
Arch Comput Methods Eng. 2023 May 27:1-47. doi: 10.1007/s11831-023-09928-7.
Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
尽管鲸鱼优化算法（WOA）简单且在解决一些优化问题上取得了成功，但它仍面临诸多问题。因此，WOA引起了学者们的关注，研究人员常常倾向于采用并改进它来解决实际应用中的优化问题。结果，已经开发出了许多WOA的变体，通常采用两种主要方法：改进和杂交。然而，尚无全面的研究对WOA及其变体进行批判性审查和分析，以找到有效的技术和算法，并开发出更成功的变体。因此，在本文中，首先对WOA进行批判性分析，然后系统地回顾WOA在过去五年中的发展。为此，引入了一种新的适应性PRISMA方法来选择符合条件的论文，包括三个主要阶段：识别、评估和报告。通过三个筛选步骤和严格的纳入标准改进了评估阶段，以选择合理数量的符合条件的论文。最终，选择了由知名出版社（包括施普林格、爱思唯尔和电气与电子工程师协会）出版的59个改进型WOA变体和57个杂交WOA变体作为符合条件的论文。描述了改进合格WOA变体的有效技术和杂交成功算法。对合格的WOA在连续、二进制、单目标和多目标/多目标类别中进行了综述。可视化了合格WOA变体在其出版商、期刊、应用和作者国家方面的分布情况。还得出结论，该领域的大多数论文缺乏与先前WOA变体的全面比较，通常仅与其他算法进行比较。最后，提出了一些未来的研究方向。
