To solve the problems that the traditional multi-threshold segmentation methods have the computational complexity increased with the increase of the number of thresholds, and have very low efficiency of multi-threshold segmentation for a given image, a multi-threshold segmentation method based on Symbiotic Organisms Search (SOS) algorithm combined with Kapur entropy threshold was proposed. Firstly, the Elite Opposition-Based Learning (EOBL) was added into the symbiotic stage of SOS algorithm, so as to solve the problem that the traditional SOS algorithms tend to fall into local optimum when dealing with complex optimization problems. Then, the Levy flight mechanism was introduced to expand the search range of SOS algorithm and enhance the randomness of the algorithm's search trajectory. Finally, the obtained Modified Symbiotic Organisms Search (MSOS) algorithm was applied to find the optimal threshold values for forest fire images. Experimental results show that compared with other optimization algorithms such as Particle Swarm Optimization (PSO) algorithm,Harmony Search Algorithm (HSA) and Bat Algorithm (BA), the MSOS algorithm has the superiority in segmenting images, so it is practical and valuable in practical engineering problems.

Aiming at the disadvantages of traditional Support Vector Machine (SVM) in the wrapper feature selection:low classification accuracy, redundant feature subset selection and poor computational efficiency, the meta-heuristic optimization algorithm was used to simultaneously optimize SVM and feature selection. In order to improve the classification effect of SVM and the ability of feature subset selection, firstly, the Spotted Hyena Optimizer (SHO) algorithm was improved by using the adaptive Differential Evolution (DE) algorithm, chaotic initialization and tournament selection strategy, so as to enhance its local search ability as well as improve its optimization efficiency and solution accuracy; secondly, the improved algorithm was applied to the simultaneous optimization of feature selection and SVM parameter adjustment; finally, a feature selection simulation experiment was carried out on the UCI datasets, and the classification accuracy, the number of selected features, the fitness value and the running time were used to comprehensively evaluate the optimization performance of the proposed algorithm. Experimental results show that the simultaneous optimization mechanism of the improved algorithm can reduce the number of selected features with high classification accuracy, and compared to the traditional algorithms, this algorithm is more suitable for solving the problem of wrapper feature selection, which has good application value.