Gao Chonghao, Pang Xinping, Wang Chongbao, Huang Jingyue, Liu Hui, Zhu Chengjiang, Jin Kunpei, Li Weiqi, Zheng Pengtao, Zeng Zihang, Wei Yanyu, Pang Chaoyang
College of Computer Science, Sichuan Normal University, Chengdu 610101, China.
West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu 610041, China.
Curr Alzheimer Res. 2024;21(5):324-341. doi: 10.2174/0115672050325388240823092338.
When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set.
Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm.
In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimer's Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality.
At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.
当蚁群优化算法（ACO）擅长识别最短路径时，在迭代过程中临时解是不确定的。所有临时解构成一个解集。
其中每个解都是随机的。也就是说，该解集具有熵。当解趋于稳定时，熵也收敛到一个固定值。因此，本文提出将熵用作蚁群优化算法的收敛准则。所提出准则的优点是它近似算法的最优收敛时间。
为了证明熵收敛准则的优越性，将其用于对从阿尔茨海默病（AD）患者中采样的基因芯片数据进行聚类。该聚类算法与六种典型聚类算法进行了比较。比较表明，以熵为收敛准则的蚁群优化算法质量良好。
同时，应用所提出的算法，我们分析了与能量代谢相关基因的聚类特征，发现随着AD的发生，能量代谢系统的熵降低；也就是说，系统无序度显著降低。
