图像匹配是一种约束最优化问题，系统是否收敛于全局最优值一直尚未解决。
Image matching belongs to constrained optimization problems. Whether the system would converge to the global optimum is still an open problem.
理论分析和仿真实验表明改进算法不仅具有以任意精度达到全局最优值的能力，而且具有更高的优化效率。
Theoretical analysis and simulation results show that AGA can not only get the global optimum value in arbitrary precision, but also raise efficiency remarkably.
算法中采用的记忆指导搜索策略重点搜索了各记忆段的局部最优值，避免了全局搜索的盲目性；
The adoption of remembrance-guided search method emphasizes local optimum value in each remembrance segment, which avoids the blindness of global search.
应用推荐