基于自适应参数多目标优化的人脸特征选择
Feature Selection for Face Based on Adaptive Parameter Multi-Objective Optimization
摘要: 针对传统多目标算法在人脸识别的特征选择问题中的搜索性能稳定性差和缓慢的收敛速度，本研究提出了一种创新的基于自适应参数的多目标优化算法(Adaptive Parameter Multi-objective Particle Swarm Optimization, APMPSO)。APMPSO通过在不同阶段合理分配全局搜索和局部搜索的力度，算法能够更高效地利用搜索资源，减少不必要的搜索过程，从而提高收敛速度和收敛精度。同时具有更好的适应性，能够根据具体的问题和数据集特点自动调整参数。通过在迭代过程中动态改变参数，算法可以更好地适应不同的搜索环境，提高在各种人脸识别问题上的性能。本算法与多种多目标优化算法在收敛速度，人脸特征选择效率问题上进行了比较，实验结果证明，APMPSO能在人脸识别问题上提供更好的帮助，显示出与粒子群算法相比的显著优势，显示出超前的应用效率。
Abstract: In view of the poor search performance stability and slow convergence speed of traditional multi-objective algorithms in the feature selection problem of face recognition, this study proposes an innovative multi-objective optimization algorithm based on adaptive parameters (Adaptive Parameter Multi-objective Particle Swarm Optimization, APMPSO). By reasonably allocating the strength of global search and local search at different stages, the APMPSO algorithm can more efficiently utilize search resources and reduce unnecessary search processes, thereby improving the convergence speed and convergence accuracy. At the same time, it has better adaptability and can automatically adjust parameters according to specific problems and data set characteristics. By dynamically changing parameters during the iteration process, the algorithm can better adapt to different search environments and improve performance on various face recognition problems. This algorithm is compared with a variety of multi-objective optimization algorithms in terms of convergence speed and face feature selection efficiency. The experimental results show that APMPSO can provide better help in face recognition problems, showing significant advantages over particle swarm algorithms and advanced application efficiency.
文章引用：张鸿鑫. 基于自适应参数多目标优化的人脸特征选择[J]. 应用数学进展, 2025, 14(4): 51-63. https://doi.org/10.12677/aam.2025.144139

参考文献

[1] Andreica, M.I., Briciu, S. and Andreica, M.E. (2009) Algorithmic Solutions to Some Transportation Optimization Problems with Applications in the Metallurgical Industry.
[2] Annavarapu, C.S., Dara, S. and Banka, H. (2016) Cancer Microarray Data Feature Selection Using Multi-Objective Binary Particle Swarm Optimization Algorithm. EXCLI Journal, 15, Article 460.
[3] Bedi, P., Bansal, R. and Sehgal, P. (2012) Multimodal Biometric Authentication Using PSO Based Watermarking. Procedia Technology, 4, 612-618. [Google Scholar] [CrossRef] 
[4] Belhumeur, P.N., Hespanha, J.P. and Kriegman, D.J. (1997) Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 711-720. [Google Scholar] [CrossRef] 
[5] 冯茜, 李擎, 全威, 裴轩墨. 多目标粒子群优化算法研究综述[J]. 工程科学学报, 2021, 43(6) :745-753.
[6] Cai, D., He, X., Han, J. and Zhang, H. (2006) Orthogonal Laplacianfaces for Face Recognition. IEEE Transactions on Image Processing, 15, 3608-3614. [Google Scholar] [CrossRef] [PubMed]
[7] Cheng, J., Liu, Q., Lu, H. and Chen, Y. (2005) Supervised Kernel Locality Preserving Projections for Face Recognition. Neurocomputing, 67, 443-449. [Google Scholar] [CrossRef] 
[8] 叶德意, 何正友, 臧天磊. 基于自适应变异粒子群算法的分布式电源选址与容量确定[J]. 电网技术, 2011, 35(6): 155-160.
[9] 夏季, 华志刚, 彭鹏, 陆潘, 张成, 陈刚. 基于非支配排序遗传算法的无约束多目标优化配煤模型[J]. 中国电机工程学报, 2011, 31(2): 85-90.
[10] 韩敏, 何泳. 基于高斯混沌变异和精英学习的自适应多目标粒子群算法[J]. 控制与决策, 2016, 31(8): 1372-1378.
[11] 陈小庆, 侯中喜, 郭良民, 等. 基于NSGA-II的改进多目标遗传算法[J]. 计算机应用, 2006, 26(10): 2453-2456.
[12] 郭鑫, 李立君. 基于人工免疫-改进粒子群优化算法的机械臂轨迹规划研究[J]. 机械传动, 2024, 48(5): 33-40.
[13] Lin, Q., Ma, Y., Chen, J., Zhu, Q., Coello, C.A.C., Wong, K., et al. (2018) An Adaptive Immune-Inspired Multi-Objective Algorithm with Multiple Differential Evolution Strategies. Information Sciences, 430, 46-64. [Google Scholar] [CrossRef] 
[14] 李飞, 刘建昌, 石怀涛, 等. 基于分解和差分进化的多目标粒子群优化算法[J]. 控制与决策, 2017, 32(3): 403-410.
[15] Garcia, C. and Delakis, M. (2004) Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 1408-1423. [Google Scholar] [CrossRef] [PubMed]
[16] 张伟, 黄卫民. 基于种群分区的多策略自适应多目标粒子群算法[J]. 自动化学报, 2022, 48(10): 2585-2599.
[17] Peng, G., Fang, Y., Peng, W., Chai, D. and Xu, Y. (2016) Multi-Objective Particle Optimization Algorithm Based on Sharing-Learning and Dynamic Crowding Distance. Optik, 127, 5013-5020. [Google Scholar] [CrossRef] 
[18] Liu, R.C., Li, J.X., Fan, J., et al. (2017) A Coevolutionary Technique Based on Multi-Swarm Particle Swarm Optimization for Dynamic Multi-Objective Optimization. European Journal of Operational Research, 261, 1028-1051.
[19] 夏立荣, 李润学, 刘启玉, 等. 基于动态层次分析的自适应多目标粒子群优化算法及其应用[J]. 控制与决策, 2015(2): 215-221.
[20] Huang, L., Shimizu, A. and Kobatake, H. (2005) Robust Face Detection Using Gabor Filter Features. Pattern Recognition Letters, 26, 1641-1649. [Google Scholar] [CrossRef] 
[21] Jang, J.-S. and Kim, J.-H. (2008) Fast and Robust Face Detection Using Evolutionary Pruning. IEEE Transactions on Evolutionary Computation, 12, 562-571. [Google Scholar] [CrossRef] 
[22] Yang, J., Frangi, A.F., Yang, J.Y., et al. (2005) KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 230-244. [Google Scholar] [CrossRef] [PubMed]
[23] Xue, B., Zhang, M. and Browne, W.N. (2013) Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach. IEEE Transactions on Cybernetics, 43, 1656-1671. [Google Scholar] [CrossRef] [PubMed]

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