Practicably Optimal Point and its Solution in Deterministic System

Ruo Ping Li; Xiang Yong Kong; Li Qun Gao; De Xuan Zou

doi:10.4028/www.scientific.net/AMM.190-191.143

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 190-191Practicably Optimal Point and its Solution in...

Practicably Optimal Point and its Solution in Deterministic System

Abstract:

In some cases, it’s not enough to consider only the objective function of the global optimal point, but also the character of this point and its nearby area. To this end, the concept of practicably optimal point is introduced in this paper. Further, a novel intelligence method named simulated particle swarm optimization (NSPSO) algorithm is proposed to obtain the practicably optimal points according to different requirements. The optimization results reveal that the NSPSO algorithm has strong convergence and stability, and appears to be an efficient alternative for obtaining the practicably optimal points according to different requirements.

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Periodical:

Applied Mechanics and Materials (Volumes 190-191)

Pages:

143-146

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.190-191.143

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Online since:

July 2012

Authors:

Ruo Ping Li, Xiang Yong Kong, Li Qun Gao, De Xuan Zou

Keywords:

Global Optimal Point, Neighborhood Characteristics, Practicably Optimal Point, Simulated Particle Swarm Optimization Algorithm, Stability

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References

[1] Kennedy J., Eberhart R. C., Particle Swarm Optimization. Proc. IEEE International Conference on Neural Networks, 1995, pp.1942-1948.

Google Scholar

[2] Pengyeng Yin, Jingyu Wang, Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique, Journal of Computational and Applied Mathematics, 216 (2008), pp.73-86.

DOI: 10.1016/j.cam.2007.04.018

Google Scholar

[3] Yannis Marinakis, Magdalene Marinaki, Georgios Dounias, Particle swarm optimization for pap-smear diagnosis, Expert Systems with Applications, 35(4) (2008), pp.1645-1656.

DOI: 10.1016/j.eswa.2007.08.089

Google Scholar

[4] Weibing Liu, Xianjia Wang, An evolutionary game based particle swarm optimization algorithm, Journal of Computational and Applied Mathematics, 214 (2008), pp.30-35.

DOI: 10.1016/j.cam.2007.01.028

Google Scholar

[5] Xueming Yang, Jinsha Yuan, Jiangye Yuan, Huina Mao, A modified particle swarm optimizer with dynamic adaptation, Applied Mathematics and Computation, 189(2) (2007), pp.1205-1213.

DOI: 10.1016/j.amc.2006.12.045

Google Scholar