College of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China.
College of Civil Aviation, Shenyang Aerospace University, Shenyang 110136, China.
Math Biosci Eng. 2021 Feb 22;18(2):1898-1925. doi: 10.3934/mbe.2021099.
Accurate and efficient estimation for defect profile of magnetic flux leakage (MFL) signals is important for nondestructive evaluation in industry. To improve the accuracy of defect profile reconstruction, an improved reconstruction method based on modified cuckoo search (CS), called MCS, is proposed in this paper. Firstly, a novel single-dimension updating evolution strategy is proposed to avoid the interference between multiple dimensions, which can make full use of the appropriate nest position in the historical search. Secondly, an adaptive multi-strategy difference evolution is introduced into the evolution process to improve the diversity and efficiency of CS algorithm. The proportion factor of each strategy in multi-strategy difference evolution is adjusted dynamically according to the value of the objective fitness. Finally, various MFL signals are selected to verify the effectiveness of the proposed MCS algorithm. The experiment results illustrate that the proposed method has high performance on the quality of the solution and robustness for noise.
准确高效地估计漏磁（MFL）信号的缺陷轮廓对于工业无损检测至关重要。为了提高缺陷轮廓重建的准确性，本文提出了一种基于改进布谷鸟搜索（CS）的改进重建方法，称为 MCS。首先，提出了一种新颖的单维更新进化策略，以避免多维之间的干扰，从而可以充分利用历史搜索中的适当巢位。其次，在进化过程中引入了自适应多策略差分进化，以提高 CS 算法的多样性和效率。多策略差分进化中各策略的比例因子根据目标适应度的值进行动态调整。最后，选择各种 MFL 信号来验证所提出的 MCS 算法的有效性。实验结果表明，该方法在解的质量和噪声鲁棒性方面具有优异的性能。
