Analysis method of competitive advantage of new industrial innovation alliance based on contraction factor particle swarm optimization (PSO)

被引:0
作者
Yuan-Qiang Lian
机构
[1] Yangzhou University,School of Business
来源
Cluster Computing | 2019年 / 22卷
关键词
Constriction factor; Particle swarm optimization; Emerging industry; Innovation union; Competitive advantage;
D O I
暂无
中图分类号
学科分类号
摘要
To improve effectiveness of competitive advantage analysis algorithm of emerging industry innovation union, a kind of competitive advantage analysis method of emerging industry innovation union based on constriction factor particle swarm optimization (PSO) is proposed. Firstly, competitive advantage evaluation model of emerging industry innovation union is constructed aimed at uncertain influence factor existing in evaluation to strategic emerging industry; secondly, particle swarm optimization is introduced, and to avoid premature convergence problem existing in particle swarm optimization and realize rapid convergence of particle to global optimal solution, constriction factor and two operators, i.e. “attraction” and “diffusion”, are introduced in this paper so that diversity of particle swarm is kept and better convergence rate is possessed. Finally, through empirical analysis to strategic emerging industry evaluation of an area, feasibility and rationality of the method are verified.
引用
收藏
页码:4291 / 4297
页数:6
相关论文
共 34 条
[1]  
Pereira I(2013)Tuning meta-heuristics using multi-agent learning in a scheduling system Trans. Comput. Sci. 2013 190-210
[2]  
Madureira A(2012)A comparative study of metaheuristic methods for transmission network expansion planning Princ. Concepts Appl. Evol. Comput. 2012 319-339
[3]  
Oliveira PB(2014)Hybrid renewable energy systems for off-grid electric power: review of substantial issues Renew. Sustain. Energy Rev. 35 527-539
[4]  
Verma AR(2014)Data-intensive applications, challenges, techniques and technologies: a survey on big data Inf. Sci. 275 314-347
[5]  
Bijwe PK(2009)Acoustofluidics: theory and simulation of streaming and radiation forces at ultrasound resonances in microfluidic devices Acoust. Soc. Am. J. 125 2592-2592
[6]  
Panigrahi B(2003)A hybrid particle swarm optimization for distribution state estimation IEEE Trans. Power Syst. 18 60-68
[7]  
Mohammed YS(2006)PSO-based neural network optimization and its utilization in a boring machine J. Mater. Process. Technol. 178 19-23
[8]  
Mustafa MW(2007)PSO-based algorithm for home care worker scheduling in the UK Comput. Indus. Eng. 53 559-583
[9]  
Bashir N(2004)Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control Electr. Power Syst. Res. 72 203-212
[10]  
Chen CLP(2002)Particle swarm optimization method for constrained optimization problems Intell. Technol. Theory Appl. 76 214-220