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 n problem. We can solve the problem with GeesePSO optimization. In this paper, an improved algorithm is proposed using the characteristics of the flight of geese for reference. The improved algorithm has superiority over PSO; for one thing, it keeps the population various by ordering all the particles and making each particle fly following its anterior particle; for another thing, it strengthens cooperation and competition between particles by making each particle share more useful information of the other particles. The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms.
Cao Chun- Hong, Wang Li- Min, Han Chun-Yan, Zhao Da-Zhe and Zhang Bin, 2012. Geese PSO Optimization in Geometric Constraint Solving. Information Technology Journal, 11: 504-507.
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