LSWOA: An enhanced whale optimization algorithm with Levy flight and Spiral flight for numerical and engineering design optimization problems

Junhao Wei, Yanzhao Gu, Zhanxi Xie, Yuzheng Yan, Baili Lu, Zikun Li, Ngai Cheong, Jiafeng Zhang and Song Zhang

PLOS ONE, 2025, vol. 20, issue 9, 1-45

Abstract: Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. Thus, an enhanced Whale Optimization Algorithm (LSWOA) based on multiple strategies is proposed, aiming to overcome the limitations of the canonical WOA. The performance of the canonical WOA is improved through innovative strategies: first, an initialization process using Good Nodes Set is introduced to ensure that the search starts from a higher-quality baseline; second, a distance-based guided search strategy is employed to adjust the search direction and intensity by calculating the distance to the optimal solution, which enhances the algorithm’s ability to escape local optima; and lastly, LSWOA introduces an enhanced spiral updating strategy, while the enhanced spiral-enveloping prey strategy effectively balances exploration and exploitation by dynamically adjusting the spiral shape parameters to adapt to different stages of the search, thereby more accurately updating the positions of individuals and improving convergence speed. In the experimental section, we validate the efficiency and superiority of LSWOA by comparing it with outstanding metaheuristic algorithms and excellent WOA variants. The experimental results show that LSWOA exhibits significant optimization performance on the benchmark functions with various dimensions. Additionally, LSWOA is tested on seven engineering design optimization problems, and the results demonstrate that it performs excellently in these application scenarios, effectively solving complex optimization problems in different dimensions and showing its potential for a wide range of applications in real-world engineering challenges.Author summary: I received my B.S. degree in Unmanned Aerial Vehicle System Engineering from Aviation College, Beijing Institute of Technology (Zhuhai) in 2023. I’m now pursuing a M.S. degree in Big Data and Internet of Things at Macao Polytechnic University. I have strong interest in metaheuristic algorithms, RRT algorithm and APF algorithm. During my MSc at Macao Polytechnic University, I worked as a research assistant, mainly responsible for the research work of UAV path planning.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322058 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 22058&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0322058

DOI: 10.1371/journal.pone.0322058

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().