Theoretical analysis and simulation results show that AGA can not only get the global optimum value in arbitrary precision, but also raise efficiency remarkably.
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。
The adoption of remembrance-guided search method emphasizes local optimum value in each remembrance segment, which avoids the blindness of global search.
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；
So firstly to get a better estimation of parameter using iterate inversion on wide scale, then using this estimation as initial value on mini scale till to get global optimum of original problem.
因此可先在粗尺度上迭代反演，得到一个较好的参数估计，再将这个估计作为较精细尺度的初值进行反演，直至原问题的全局最优解。
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