Ensemble learning of swarm intelligence evolutionary algorithm of artificial neural network (ANN) is one of the core research directions in the field of artificial intelligence (AI). As a representative member of swarm intelligence evolutionary algorithm, shuffled frog leaping algorithm (SFLA) has the advantages of simple structure, easy implementation, short operation time, and strong global optimization ability. However, SFLA is susceptible to fall into local optimas in the face of complex and multi-dimensional symmetric function optimization, which leads to the decline of convergence accuracy. This paper proposes an improved shuffled frog leaping algorithm of threshold oscillation based on simulated annealing (SA-TO-SFLA). In this algorithm, the threshold oscillation strategy and simulated annealing strategy are introduced into the SFLA, which makes the local search behavior more diversified and the ability to escape from the local optimas stronger. By using multi-dimensional symmetric function such as drop-wave function, Schaffer function N.2, Rastrigin function, and Griewank function, two groups (i: SFLA, SA-SFLA, TO-SFLA, and SA-TO-SFLA; ii: SFLA, ISFLA, MSFLA, DSFLA, and SA-TO-SFLA) of comparative experiments are designed to analyze the convergence accuracy and convergence time. The results show that the threshold oscillation strategy has strong robustness. Moreover, compared with SFLA, the convergence accuracy of SA-TO-SFLA algorithm is significantly improved, and the median of convergence time is greatly reduced as a whole. The convergence accuracy of SFLA algorithm on these four test functions are 90%, 100%, 78%, and 92.5%, respectively, and the median of convergence time is 63.67 s, 59.71 s, 12.93 s, and 8.74 s, respectively; The convergence accuracy of SA-TO-SFLA algorithm on these four test functions is 99%, 100%, 100%, and 97.5%, respectively, and the median of convergence time is 48.64 s, 32.07 s, 24.06 s, and 3.04 s, respectively.

The processing of gamma spectrum measurement data has always been an important step in data interpretation of the survey area. It includes the correction of radioactivity interference caused by non-measuring targets, the correction of effects caused by different heights or topography in the survey environment, the smoothness and denoising of the measurement spectrum, etc. The accuracy of each step will affect the final data resources. The interpretation of the materials results in inaccurate regional radioactivity assessment. Maximum noise separation transformation is a similar method to principal component transformation, which is used to reduce dimensionality and noise of data. This paper takes the noise reduction process of gamma spectrum data processing as the research direction, uses SPSS data processing software, combines with MATLAB to denoise the data, and uses the maximum noise separation transformation as the denoising method, applies it to denoise the spectral data, and uses the principal component analysis method and the maximum noise fraction method to denoise the same set of data separately. After analysis, it is concluded that the maximum noise fraction method is superior to principal component analysis when there is a large noise variance for a set of data.