DocumentCode :
2676542
Title :
The Application of the Genetic algorithm-Ant algorithm in the Geometric Constraint SatisfactionGuidelines
Author :
Chunhong, Cao ; Bin, Zhang ; Limin, Wang ; Wenhui, Li
Author_Institution :
Inf. Sci. & Eng., Northeastern Univ., Shenyang
Volume :
1
fYear :
2006
fDate :
17-19 July 2006
Firstpage :
101
Lastpage :
106
Abstract :
The constraint problem can be transformed to an optimization problem. We introduce GAAA (genetic algorithm-ant algorithm) in solving geometric constraint problems. We adopt genetic algorithm in the former process of algorithm so that it can make use of the fastness, randomicity and global stringency of genetic algorithm. Its result is to produce the initiatory distribution of information elements. The latter process of the algorithm we adopt ant algorithm. In the condition that there are some initiatory information elements, we can utilize fully the parallel, feedback and the high solving efficiency. Using random colony in the genetic algorithm, this can not only improve the speed of ant algorithm but also avoid getting in the local best solution when solving the precise solutions. The algorithm has a good effect in not only optimization capability but also time capability. Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially
Keywords :
constraint theory; genetic algorithms; geometry; random processes; genetic algorithm-ant algorithm; geometric constraint satisfaction guidelines; optimization problem; random colony; Ant colony optimization; Cognitive informatics; Constraint optimization; Feedback; Genetic algorithms; Genetic engineering; Information science; Nonlinear equations; Particle swarm optimization; Stochastic systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0475-4
Type :
conf
DOI :
10.1109/COGINF.2006.365683
Filename :
4216398
Link To Document :
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