Tang Kankan, Zhang Lin
Department of Respiratory and Critical Care Medicine, the First Medical Center, Chinese PLA General Hospital, Beijing, Haidian District, China.
PLoS One. 2025 Jun 3;20(6):e0325272. doi: 10.1371/journal.pone.0325272. eCollection 2025.
Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. This study proposes an enhanced version of the WOA, named the Outpost-based Multi-population Whale Optimization Algorithm (OMWOA), which integrates two key mechanisms: the outpost mechanism and a multi-population enhanced mechanism. These modifications aim to improve the algorithm's performance in terms of solution accuracy and convergence rate. The effectiveness of OMWOA is thoroughly evaluated by benchmarking it against state-of-the-art evolutionary algorithms from the IEEE CEC 2017 and IEEE CEC 2022 competitions. Additionally, this study provides a detailed analysis of the influence of the outpost and multi-population mechanisms on OMWOA's performance, as well as its scalability in problems of varying dimensionalities. To validate its applicability in real-world problems, the proposed algorithm is combined with Kernel Extreme Learning Machine (KELM) for solving medical disease diagnosis tasks. The experimental results demonstrate the superior performance of OMWOA in terms of diagnostic accuracy across five medical datasets, highlighting its potential for real-world applications.
群体智能优化算法是自然启发式计算方法的一个重要分支，旨在通过模拟生物系统的集体行为来解决复杂的优化问题。鲸鱼优化算法（WOA）是一种新开发的元启发式算法，主要基于座头鲸在海洋中的捕食行为。本研究提出了一种WOA的增强版本，即基于前哨的多群体鲸鱼优化算法（OMWOA），它集成了两个关键机制：前哨机制和多群体增强机制。这些改进旨在提高算法在求解精度和收敛速度方面的性能。通过与2017年IEEE CEC和2022年IEEE CEC竞赛中的先进进化算法进行基准测试，全面评估了OMWOA的有效性。此外，本研究详细分析了前哨和多群体机制对OMWOA性能的影响，以及它在不同维度问题上的可扩展性。为了验证其在实际问题中的适用性，将所提出的算法与核极限学习机（KELM）相结合，用于解决医学疾病诊断任务。实验结果表明，OMWOA在五个医学数据集的诊断准确性方面具有卓越性能，突出了其在实际应用中的潜力。
