1
|
Li R, Zhuang Q, Yu N, Li R, Zhang H. Improved Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration
Coefficients for Transient Electromagnetic Inversion. Curr Bioinform 2022. [DOI: 10.2174/1574893616666210727164226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Recently, Particle Swarm Optimization (PSO) has been increasingly used in
geophysics due to its simple operation and fast convergence.
Objective:
However, PSO lacks population diversity and may fall to local optima. Hence, an Improved
Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration Coefficients (IH-PSO-SCAC) is proposed
and successfully applied to test functions in Transient Electromagnetic (TEM) nonlinear inversion.
Method:
A reverse learning strategy is applied to optimize population initialization. The sine-cosine
acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population
diversity during the search process. In addition, the mutation method is used to reduce the probability
of premature convergence.
Results:
The application of IH-PSO-SCAC in the test functions and several simple layered models are
demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test
our algorithm. The first model contains an underground low-resistivity anomaly body and the second
model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases,
resistivity profiles are obtained, and the inverse problem is solved for verification.
Conclusion:
The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied
in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.
Collapse
Affiliation(s)
- Ruiheng Li
- College of Electrical Engineering, Chongqing University, Chongqing, 400044, China
- State Key Laboratory of Power
Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400044, China
| | - Qiong Zhuang
- College of Electrical Engineering, Chongqing University, Chongqing, 400044, China
- State Key Laboratory of Power
Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400044, China
| | - Nian Yu
- College of Electrical Engineering, Chongqing University, Chongqing, 400044, China
- State Key Laboratory of Power
Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400044, China
| | - Ruiyou Li
- College of Electrical Engineering, Chongqing University, Chongqing, 400044, China
- State Key Laboratory of Power
Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400044, China
| | - Huaiqing Zhang
- College of Electrical Engineering, Chongqing University, Chongqing, 400044, China
- State Key Laboratory of Power
Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400044, China
| |
Collapse
|
2
|
Lv Y, Huang S, Zhang T, Gao B. Application of Multilayer Network Models in Bioinformatics. Front Genet 2021; 12:664860. [PMID: 33868392 PMCID: PMC8044439 DOI: 10.3389/fgene.2021.664860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 02/26/2021] [Indexed: 11/24/2022] Open
Abstract
Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.
Collapse
Affiliation(s)
- Yuanyuan Lv
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| |
Collapse
|