The Application of Improved PSO Algorithm in the Geometric Constraint Solving

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International Journal of Advanced Network, Monitoring and Controls

Xi'an Technological University

Subject: Computer Science, Software Engineering

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eISSN: 2470-8038

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VOLUME 2 , ISSUE 3 (September 2017) > List of articles

The Application of Improved PSO Algorithm in the Geometric Constraint Solving

Tian Wei / Zhu Xiaogang

Keywords : PSO, Geometric constraint solving, geese, individual extreme, global extreme

Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 3, Pages 116-119, DOI: https://doi.org/10.1109/iccnea.2017.70

License : (CC BY-NC-ND 4.0)

Published Online: 12-April-2018

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ABSTRACT

Geometric constraint solving is a hot topic in the constraint design research field. Particle swarm optimization (PSO) is a method to solve the optimization problem from the biological population’s behavior characteristics. PSO is easy to diverge and fall into the local optimum. There are various kinds of improvements. In addition to improving some performance, the corresponding cost is paid. In this paper, a particle swarm optimization algorithm based on the geese is adopted to solve the geometric constraint problem. The algorithm is inspired by the flight characteristics of geese; each particle follows the optimal particle in front of it to keep the diversity; each particle can share more useful information of other particles, which strengthens cooperation and competition between particles. The algorithm balances the contradiction between the search speed and the accuracy of the algorithm to a certain extent. Experimental results show that the proposed algorithm can improve the efficiency and convergence of geometric constraint solving.

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REFERENCES

Yuan Bo. The research and implement of geometric constraint solving[D]. Beijing:Tsinghua University,1999.

 

Holland J H. Adaptation in natural and artificial systems[M] Cambridge: MIT Press,1975..

 

Liu Jin-yang,Guo Mao-zu,Deng Chao. GeesePSO: an efficient Improvement to particle swarm optimization [J]. Computer Sci-ence,2006,33(11):166-168.

 

Beekman M, Rantnieks FLW. Long-range foraging by the Honey-bee, Apis Mellifera L. Functional Ecologicy, 2000, (14): 490-496

 

Wilson E O. Sociobiology: The New Synthesis [M]. Cambridge: Belknap Press, 1975.

 

Shi Zhi-liang,Chen Li-ping. A simplified iterative algorithm to solve geometric constraints[J], Journal of Computer-Aided Design & Computer Graphics,2006,18(6):787-792..

 

Sun Wei,Ma Tie-qiang,Huang Yu-jun.Research on method of constraint conversion in feature-based data exchange between heter-ogeneous CAD systems[J] . Journal of Mechanical Science and Technology,2009,23(1):246-253..

 

Liu Sheng-li,Tang Min,Dong Jin-xiang.Geometric constraint satisfaction using genetic simulated annealing algorithm [J]. Journal of Computer Aided-design & Computer Graphics,2003,15 (8):1011-1029.

 

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