基于自适应差分进化算法的高维模糊度搜索
Searching High-dimension Ambiguity Based on Self-adaptive Differential Evolution Algorithm
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摘要: 针对高维整周模糊度解算问题,提出了一种新的搜索算法,采用自适应差分进化算法,利用其特有的全局、快速、并行搜索的特性对高维模糊度进行固定。根据所求解问题的特点,在原有自适应差分进化算法的基础上对部分参数进行重新设定,从而实现模糊度的快速搜索。并以LAMBDA算法的解算结果和运算速率为依据,验证本算法结果的正确性和解算的快速性。通过模拟和实测不同维数的数据进行验证,表明该算法对高维模糊度解算具有一定的应用参考价值,且具有较好的可靠性和鲁棒性。Abstract: In this paper, a new algorithm is proposed to solve the problem of high dimensional ambiguity resolution. The self-adaptive differential evolution algorithm is used to fix the high dimensional ambiguity with its global, fast and parallel search. According to the characteristics of the problem to be solved, some parameters are reset on the basis of the original adaptive differential evolution algorithm so as to realize the quick search of the ambiguity. Based on the solution and operation rate of LAMBDA algorithm, the correctness of the algorithm and the rapidity of solution are verified. It is proved that the algorithm has certain application reference value for high dimensional ambiguity resolution, and it has good reliability and robustness by simulating and measuring the data with different dimensions.