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

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

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

Authors: Gao C.¹, Pang X.², Wang C.³, Huang J.¹, Liu H.⁴, Zhu C.¹, Jin K.¹, Li W.¹, Zheng P.¹, Zeng Z.¹, Wei Y.⁵, Pang C.¹
Affiliations:
1. College of Computer Science, Sichuan Normal University
2. West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University
3. , Beijing Magicnurse Surgical Robot Technology Co. Ltd.
4. School of Information and Software Engineering, University of Electronic Science and Technology of China
5. National Key Laboratory of Science and Technology on Vacuum Electronics, School of Electronic Science and Engineering, University of Electronic Science and Technology of China
Issue: Vol 21, No 5 (2024)
Pages: 324-341
Section: Medicine
URL: https://ruspoj.com/1567-2050/article/view/643809
DOI: https://doi.org/10.2174/0115672050325388240823092338
ID: 643809

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Abstract

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 Alzheimers 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.

Keywords

Ant colony algorithms, entropy, Alzheimer&, rsquo, s disease, gene chip data analysis, entropy convergence criterion, energy metabolism system.

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

Full Text

Abstract

Keywords

About the authors

Supplementary files