Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Comput Biol Med. 2022 May;144:105356. doi: 10.1016/j.compbiomed.2022.105356. Epub 2022 Mar 7.
Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.
分类模型，如多宇宙优化（MVO），在疾病诊断中起着至关重要的作用。为了提高 MVO 的效率和准确性，本文缓解了 MVO 的缺陷，并将改进后的 MVO 与核极限学习机（KELM）相结合，用于有效的疾病诊断。虽然 MVO 在一些感兴趣的问题上取得了一些相对较好的结果，但它在某些多方面的盆地（特别是高维的多峰问题）中存在着收敛速度慢和陷入局部最优的问题。为了解决这些缺点，在本研究中，提出了一种新的混沌模拟退火大修 MVO（CSAMVO）。基于 MVO，采用两种方法提供相对稳定和高效的收敛速度。具体来说，应用混沌强化机制（CIP）到最优宇宙评估阶段，以增加宇宙搜索的深度。在获得较为满意的结果后，采用模拟退火算法（SA）来增强 MVO 避免局部最优的能力。为了评估其性能，将所提出的 CSAMVO 方法与三十九个基准函数的广泛的经典算法进行了比较。结果表明，改进的 MVO 在解的质量和收敛速度方面优于其他算法。此外，基于 CSAMVO，建立了一种称为 CSAMVO-KELM 的混合 KELM 模型，用于疾病诊断。为了评估其有效性，将新的混合系统与两个疾病诊断问题上的多种竞争分类器进行了比较。结果表明，所提出的 CSAMVO 辅助分类器可以找到具有更好学习潜力和更高预测性能的解决方案。
