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Cluster Computing

Erschienen in:

30.01.2018

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

verfasst von: Yuan-Qiang Lian

Erschienen in: Cluster Computing | Sonderheft 2/2019

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Abstract

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.

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Pereira, I., Madureira, A., Oliveira, P.B., et al.: Tuning meta-heuristics using multi-agent learning in a scheduling system. Trans. Comput. Sci. 2013, 190–210 (2013)

Verma, A.R., Bijwe, P.K., Panigrahi, B.: A comparative study of metaheuristic methods for transmission network expansion planning. Princ. Concepts Appl. Evol. Comput. 2012, 319–339 (2012)

Mohammed, Y.S., Mustafa, M.W., Bashir, N.: Hybrid renewable energy systems for off-grid electric power: review of substantial issues. Renew. Sustain. Energy Rev. 35, 527–539 (2014)

Chen, C.L.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)

Bruus, H.: Acoustofluidics: theory and simulation of streaming and radiation forces at ultrasound resonances in microfluidic devices. Acoust. Soc. Am. J. 125(4), 2592–2592 (2009)

Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, IEEE pp. 84–88 (2000)

Krohling, R.A.: Gaussian swarm: a novel particle swarm optimization algorithm. In: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, IEEE pp. 372–376 (2004)

Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium on SIS, pp. 120–127 (2007)

Naka, S., Genji, T., Yura, T., et al.: A hybrid particle swarm optimization for distribution state estimation. IEEE Trans. Power Syst. 18(1), 60–68 (2003)

10.

Zhou, J., Duan, Z., Li, Y., et al.: PSO-based neural network optimization and its utilization in a boring machine. J. Mater. Process. Technol. 178(1), 19–23 (2006)

11.

Akjiratikarl, C., Yenradee, P., Drake, P.R.: PSO-based algorithm for home care worker scheduling in the UK. Comput. Indus. Eng. 53(4), 559–583 (2007)

12.

Ghoshal, S.P.: Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electr. Power Syst. Res. 72(3), 203–212 (2004)

13.

Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. Intell. Technol. Theory Appl. 76(1), 214–220 (2002)

14.

Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recogn. Lett. (2017) https://doi.org/10.1016/j.patrec.2017.07.002

15.

Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recogn. Lett. 94, 112–117 (2017)

16.

Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)

Titel: Analysis method of competitive advantage of new industrial innovation alliance based on contraction factor particle swarm optimization (PSO)
verfasst von: Yuan-Qiang Lian
Publikationsdatum: 30.01.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1863-2

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