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ITGO: Invasive tumor growth optimization algorithm

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WOS被引频次:1
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成果类型:
期刊论文
作者:
Tang, Deyu;Dong, Shoubin;Jiang, Yi;Li, Huan;Huang, Yishuan
通讯作者:
Dong, SB
作者机构:
[Tang, Deyu; Huang, Yishuan] School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, China
[Tang, Deyu; Dong, Shoubin; Huang, Yishuan] School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
[Li, Huan] Basic Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
[Jiang, Yi] Department of Mathematics and Statistics, Georgia State University, Atlanta, United States
通讯机构:
[Dong, Shoubin] S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China.
语种:
英文
关键词:
Invasive tumor growth;Meta-heuristic algorithm;Levy flight;Swarm intelligence;Evolutionary computation
期刊:
Applied Soft Computing Journal
ISSN:
1568-4946
年:
2015
卷:
36
页码:
670-698
文献类别:
WOS:Article;EI:Journal article (JA)
所属学科:
ESI学科类别:计算机科学;WOS学科类别:Computer Science, Artificial Intelligence;Computer Science, Interdisciplinary Applications
入藏号:
WOS:000360424700054;EI:20153501221970
基金类别:
National Natural Science Foundation [61070092/F020504]; building of strong Guangdong Province for Chinese Medicine Scientific Research [20141165]; Humanities and social science fund project for Guangdong Pharmaceutical University [RWSK201409]; NSFC, Research on reasoning of behavior trust for resisting collusive reputation attack [71401045]; GuangDong Provincial Natural fund, Ukraine Senate Xingnao neuroprotective effect mechanism of dynamic network based on network pharmacology [2014A030313585]; Guangdong Province Youth Innovation Talent Project, based on the cognitive rules of the semi supervised key algorithm and its cancer pattern recognition [2014KQNCX139]; Major Science And Technology project of Guangdong province [2014B010112006]; Guangdong Natural Science Foundation, major basic research and training talents project
机构署名:
本校为第一机构
院系归属:
医药信息工程学院
摘要:
This paper proposes a new optimization algorithm named ITGO (Invasive Tumor Growth Optimization) algorithm based on the principle of invasive tumor growth. The study of tumor growth mechanism shows that each cell of tumor strives for the nutrient in their microenvironment to grow and proliferate. In ITGO algorithm, tumor cells were divided into three categories: proliferative cells, quiescent cells and dying cells. The cell movement relies on the chemotaxis, random walk of motion and interaction with other cells in different categories. Invasive behaviors of proliferative cells and quiescent cells are simulated by levy flight and dying cells are simulated through interaction with proliferative cells and quiescent cells. In order to test the effectiveness of ITGO algorithm, 50 functions from CEC2005, CEC2008, CEC2010 and a support vector machine (SVM) parameter optimization problem were used to compare ITGO with other well-known heuristic optimization methods. Statistical analysis using Friedman test and Wilcoxon signed-rank statistical test with Bonferroni-Holm correction demonstrates that the ITGO algorithm is better in solving global optimization problems in comparison to the other meta-heuristic algorithms. ©2015 Elsevier B.V. All rights reserved.
参考文献:
ABERCROM.M, 1970, IN VITRO CELL DEV B, V6, P128
DORIE MJ, 1982, EXP CELL RES, V141, P201, DOI 10.1016/0014-4827(82)90082-9
Neri F, 2013, INFORM SCIENCES, V239, P96, DOI 10.1016/j.ins.2013.03.026
Rashedi E, 2009, INFORM SCIENCES, V179, P2232, DOI 10.1016/j.ins.2009.03.004
Passino KM, 2002, IEEE CONTR SYST MAG, V22, P52, DOI 10.1109/MCS.2002.1004010

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