{"title":"用熵作为蚁群优化的收敛标准及在基因芯片数据分析中的应用","authors":"Chonghao Gao, Xinping Pang, Chongbao Wang, Jingyue Huang, Hui Liu, Chengjiang Zhu, Kunpei Jin, Weiqi Li, Pengtao Zheng, Zihang Zeng, Yanyu Wei, Chaoyang Pang","doi":"10.2174/0115672050325388240823092338","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":94309,"journal":{"name":"Current Alzheimer research","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis.\",\"authors\":\"Chonghao Gao, Xinping Pang, Chongbao Wang, Jingyue Huang, Hui Liu, Chengjiang Zhu, Kunpei Jin, Weiqi Li, Pengtao Zheng, Zihang Zeng, Yanyu Wei, Chaoyang Pang\",\"doi\":\"10.2174/0115672050325388240823092338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":94309,\"journal\":{\"name\":\"Current Alzheimer research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Alzheimer research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0115672050325388240823092338\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Alzheimer research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115672050325388240823092338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
引言蚁群优化算法(ACO)善于识别最短路径,但在迭代过程中,临时解是不确定的。所有临时解构成一个解集:每个解都是随机的。也就是说,解集具有熵。当解决方案趋于稳定时,熵也会收敛到一个固定值。因此,本文提出将熵作为 ACO 的收敛标准。所提标准的优点在于它近似于算法的最佳收敛时间:为了证明熵收敛准则的优越性,本文使用熵收敛准则对基因芯片数据进行聚类,这些数据取自阿尔茨海默病(AD)患者。该聚类算法与六种典型的聚类算法进行了比较。比较结果表明,使用熵作为收敛标准的 ACO 具有良好的质量:同时,我们应用所提出的算法分析了与能量代谢相关的基因的聚类特征,发现随着 AD 的发生,能量代谢系统的熵会下降,即系统的无序性会显著降低。
Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis.
Introduction: 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.
Method: 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.
Results: 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.
Conclusion: 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.