DocumentCode :
2254676
Title :
An improved particle swarm optimization algorithm for geometric constraint solving problem
Author :
Cao, Chun-hong ; Zhang, Chang-sheng ; Wang, Li-Min
Author_Institution :
Collge of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Volume :
4
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
1335
Lastpage :
1838
Abstract :
Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. We can solve the problem by an improved PSO algorithm (IPSO), which is based on the “alldifferent” constraint. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The experiment indicates that the algorithm can be used to solve geometric constraint problem effectively.
Keywords :
constraint theory; genetic algorithms; geometry; nonlinear equations; particle swarm optimisation; alldifferent constraint; genetic operator; geometric constraint solving problem; improved PSO algorithm; mutation operator; nonlinear equation; optimization problem; particle swarm optimization; Algorithm design and analysis; Convergence; Equations; Mathematical model; Optimization; Particle swarm optimization; Schedules; Fitness computation; Geometric constraint; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
Type :
conf
DOI :
10.1109/ICMLC.2010.5580958
Filename :
5580958
Link To Document :
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