Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis


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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.

Methods: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.

作者简介

Chonghao Gao

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Xinping Pang

West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University

Email: info@benthamscience.net

Chongbao Wang

, Beijing Magicnurse Surgical Robot Technology Co. Ltd.

Email: info@benthamscience.net

Jingyue Huang

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Hui Liu

School of Information and Software Engineering, University of Electronic Science and Technology of China

Email: info@benthamscience.net

Chengjiang Zhu

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Kunpei Jin

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Weiqi Li

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Pengtao Zheng

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Zihang Zeng

College of Computer Science, Sichuan Normal University

Email: info@benthamscience.net

Yanyu Wei

National Key Laboratory of Science and Technology on Vacuum Electronics, School of Electronic Science and Engineering, University of Electronic Science and Technology of China

编辑信件的主要联系方式.
Email: info@benthamscience.net

Chaoyang Pang

College of Computer Science, Sichuan Normal University

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Email: info@benthamscience.net

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