发表机构
VŠB-Technical University of Ostrava; Torrens University Australia; Nanjing University of Information Science and Technology（俄斯特拉发理工大学; 澳大利亚托伦斯大学; 南京信息工程大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于人工原生动物优化器的膜算法MAAPO，融合膜计算与RFDB机制，在CEC2017及多级阈值图像分割中优于12种先进算法。
AI 中文摘要
本文提出了一种基于人工原生动物优化器的新型膜算法（MAAPO），用于解决全局优化问题。由于人工原生动物优化器（APO）的新颖性和竞争性能，它被选为基础元启发式算法。MAAPO整合了两项关键创新：（1）膜计算（MC）框架，引入并行分布式范式以提高种群多样性和搜索动态；（2）APO内增强的自养模型，采用基于轮盘赌的适应度-距离平衡（RFDB）机制进行自适应参考点选择。这些策略共同增强了算法的探索-开发平衡和全局搜索能力。为验证其性能，MAAPO在CEC2017测试套件上与12种先进算法进行了对比测试，并进一步应用于以Otsu和Kapur熵为目标函数的多级阈值图像分割问题。使用峰值信噪比（PSNR）、结构相似性指数（SSIM）和特征相似性指数（FSIM）指标评估分割图像的质量。实验结果表明，MAAPO优于其对比算法，提供了更优越的分割质量。这项关于MAAPO的研究为元启发式算法贡献了一种有效的增强策略，并为复杂的图像分割任务引入了一种新颖且高度适用的方法。
英文摘要
This paper proposes a novel membrane algorithm based on artificial protozoa optimizer (MAAPO) for global optimization problems. The artificial protozoa optimizer (APO) is adopted as the base meta-heuristic algorithm due to its novelty and competitive performance. MAAPO integrates two key innovations:(1) a membrane computing (MC) framework that introduces a parallel distributed paradigm to improve population diversity and search dynamics, and (2) an enhanced autotrophic model within APO that uses a roulette-based fitness-distance balance (RFDB) mechanism for adaptive reference point selection. These strategies collectively enhance the algorithm's exploration-exploitation balance and global search capabilities. To validate its performance, MAAPO is tested against 12 advanced algorithms on the CEC2017 test suite, and further applied to the multilevel thresholding image segmentation problem using Otsu and Kapur entropy as objective functions. The quality of segmented images is assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) metrics. Experimental results demonstrate that MAAPO outperforms its counterparts, delivering superior segmentation quality. This research on MAAPO contributes an effective enhancement strategy to meta-heuristic algorithms and introduces a novel, highly applicable approach for complex image segmentation tasks.
DOI：10.1007/s10462-025-11319-2
