Improved Multi-Strategy Matrix Particle Swarm Optimization for DNA Sequence Design

被引：3
作者：
Zhang, Wenyu [1 ]
Zhu, Donglin [2 ]
Huang, Zuwei [3 ]
Zhou, Changjun [2 ]
机构：
[1] Ocean Univ China, Fac Informat Sci & Engn, Qingdao 266100, Peoples R China
[2] Zhejiang Normal Univ, Coll Math & Comp Sci, Jinhua 321004, Peoples R China
[3] Jiangxi Univ Sci & Technol, Sch Informat Engn, Ganzhou 341000, Peoples R China
来源：
ELECTRONICS | 2023年 / 12卷 / 03期
基金：
中国国家自然科学基金;
关键词：
DNA computing; DNA sequences design; improved matrix particle swarm optimization algorithm (IMPSO); opposition-based learning; signal-to-noise ratio distance; ALGORITHM; INFORMATION; GENERATION;
D O I：
10.3390/electronics12030547
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency.
引用
收藏
页数：21
相关论文
共 37 条
  • [21] MilMan V.D., 1971, FUNCTIONAL ANAL APPL, V5, P288, DOI 10.1007/BF01086740
  • [22] Poli R., 2007, INTELL, V1, P57, DOI [10.1007/s11721-007-0002-0, DOI 10.1007/S11721-007-0002-0]
  • [23] Rahnamayan S, 2014, 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), P1800, DOI 10.1109/CEC.2014.6900329
  • [24] Enzyme-free nucleic acid logic circuits
    Seelig, Georg
    Soloveichik, David
    Zhang, David Yu
    Winfree, Erik
    [J]. SCIENCE, 2006, 314 (5805) : 1585 - 1588
  • [25] Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing
    Shin, SY
    Lee, IH
    Kim, D
    Zhang, BT
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2005, 9 (02) : 143 - 158
  • [26] Opposition-based learning: A new scheme for machine intelligence
    Tizhoosh, Hamid R.
    [J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 1, PROCEEDINGS, 2006, : 695 - 701
  • [27] Nearest-neighbor thermodynamics of deoxyinosine pairs in DNA duplexes
    Watkins, NE
    SantaLucia, J
    [J]. NUCLEIC ACIDS RESEARCH, 2005, 33 (19) : 6258 - 6267
  • [28] MOLECULAR STRUCTURE OF NUCLEIC ACIDS - A STRUCTURE FOR DEOXYRIBOSE NUCLEIC ACID
    WATSON, JD
    CRICK, FHC
    [J]. NATURE, 1953, 171 (4356) : 737 - 738
  • [29] Xiao JH, 2013, MATCH-COMMUN MATH CO, V70, P987
  • [30] IWO Algorithm Based on Niche Crowding for DNA Sequence Design
    Yang, Gaijing
    Wang, Bin
    Zheng, Xuedong
    Zhou, Changjun
    Zhang, Qiang
    [J]. INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES, 2017, 9 (03) : 341 - 349
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