Concerning the problem that the interpretable mapping relationship between data features and data categories do not be revealed by general feature selection algorithms. on the basis of Gene Expression Programming (GEP),by introducing the initialization methods, mutation strategies and fitness evaluation methods,an improved Feature Selection classification algorithm based on Layer Distance for GEP(FSLDGEP) was proposed. Firstly,the selection probability was defined to initialize the individuals in the population directionally, so as to increase the number of effective individuals in the population. Secondly, the layer neighborhood of the individual was proposed, so that each individual in the population would mutate based on its layer neighborhood, and the blind and unguided problem in the process of mutation was solved。Finally, the dimension reduction rate and classification accuracy were combined as the fitness value of the individual, which changed the population evolutionary mode of single optimization goal and balanced the relationship between the above two. The 5-fold and 10-fold verifications were performed on 7 datasets, the functional mapping relationship between data features and their categories was given by the proposed algorithm, and the obtained mapping function was used for data classification. Compared with Feature Selection based on Forest Optimization Algorithm (FSFOA), feature evaluation and selection based on Neighborhood Soft Margin (NSM), Feature Selection based on Neighborhood Effective Information Ratio (FS-NEIR)and other comparison algorithms, the proposed algorithm has obtained the best results of the dimension reduction rate on Hepatitis, Wisconsin Prognostic Breast Cancer (WPBC), Sonar and Wisconsin Diagnostic Breast Cancer (WDBC) datasets, and has the best average classification accuracy on Hepatitis, Ionosphere, Musk1, WPBC, Heart-Statlog and WDBC datasets. Experimental results shows that the feasibility, effectiveness and superiority of the proposed algorithm in feature selection and classification are verified.

To solve the Split Delivery Vehicle Routing Problem (SDVRP) more reasonably, overcome the shortcoming that the traditional two-stage solution method of first route and then optimization is easy to fall into local optimization, and handle the problem that the intelligent optimization algorithm fails to integrate competition and cooperation organically in the optimization stage, an Improved Pyramid Evolution Strategy (IPES) was proposed with the shortest delivery path and the least delivery vehicles as the optimization objectives. Firstly, based on the pyramid, the encoding and decoding methods and hierarchical cooperation strategy were proposed to solve SDVRP. Secondly, according to the characteristics such as the random of genetic algorithm, high parallelism of "survival of the fittest" and self-adaption, as well as the different labor division of different layers of pyramid structure, an adaptive neighborhood operator suitable for SDVRP was designed to make the algorithm converge fast to the optimum. Finally, the optimal solution was obtained. Compared with the piecewise solving algorithm, clustering algorithm, particle swarm algorithm, artificial bee colony algorithm, taboo search algorithm,the results of four simulation experiments show that, when solving the optimal path of each case, the proposed IPES has the solution accuracy improved by at least 0.92%, 0.35%, 3.07%, 9.40% respectively, which verifies the good performance of IPES in solving SDVRP.

Since the ineffectiveness of color features extracted by global and local feature extraction methods to describe the artistic style of painter, a new oil painting description method based on key region was proposed. Firstly, the information richness of oil painting region was defined by incorporating the proportion of primary color and color diversity to detect and select the key region of an oil painting. Secondly, the color features in 54 dimensions of the selected key region were used to describe the oil painting, those features were evaluated by Fisher Score, and the important features were selected to describe the key region of the oil painting, so as to highly reflect the painter artistic style. Finally, to verify the validity of the proposed method, the Naive Bayes classifier was used to realize oil painting classification. Two databases were established to perform simulation experiments. The database 1 includes 120 oil paintings by three painters, and the database 2 includes 513 oil paintings by nine painters from three different schools. The experimental results on database 1 show that, the accuracy of classification combined with Fisher Score is higher than the accuracy of ordinary classification, the accuracy of the proposed method for classifying oil paintings according to painter and school is 90% and 90.20% respectively. The experimental results on database 2 show that the accuracy of the proposed method for classifying oil paintings according to painter reaches 90%, which is 6.67 percentage points higher than that of Feature selected by Fisher Score ( Features-FS ) , and the accuracy of the proposed method for classifying oil paintings according to school is 90.20%, which is comparable to that of Features-FS. The features extracted by the proposed oil painting description method based on key region can effectively describe the artistic style of painter.

For the color quantization problem of color images, the K-means clustering algorithm has strong dependence on initial conditions and is easy to fall into local optimum, and the traditional intelligent optimization algorithms only consider the mutual competition between individuals in the population layer and ignores the mutual cooperation between the population layers. To solve the problems, a K-means-based PES (Pyramid Evolution Strategy) color image quantization algorithm was proposed. Firstly, the clustering loss function in K-means clustering algorithm was used as the fitness function of the new algorithm; secondly, PES was used for the population initialization, layering, exploration, acceleration and clustering of the colors; finally, the new algorithm was used to quantify four standard color test images at different color quantization levels. The experimental results show that the proposed algorithm can improve the defects of the K-means clustering algorithm and the traditional intelligent algorithm. Under the criterion of intra-class mean squared error, the average distortion rate of the image quantized by the new algorithm is 12.25% lower than that quantized by the PES-based algorithm, 15.52% lower than that quantized by the differential evolution algorithm, 58.33% lower than that quantized by the Particle Swarm Optimization (PSO) algorithm, 15.06% lower than that quantized by the K-means algorithm; and the less the color quantization levels, the more the image distortion rate reduced quantized by the new algorithm than that quantized by other algorithms. In addition, the visual effect of the image quantized by the proposed algorithm is better than that quantized by other algorithms.

Aiming at the problem that basic Krill Herd (KH) algorithm has poor local search ability and insufficient exploitation capacity on complex function optimization problems, a Krill Herd algorithm based on Dynamic Pressure Control operator (DPCKH) was proposed. A new dynamic pressure control operator was added to the basic krill herd algorithm, which made it more effective on complex function optimization problems. The dynamic pressure control operator quantified the induction effects of several different outstanding individuals on the target individual through Euclidean distance, accelerating the production of new krill individuals near the excellent individuals and improving the local exploration ability of krill individuals. Compared to ACO (Ant Colony Optimization) algorithm, DE algorithm, KH algorithm, KHLD (Krill Herd with Linear Decreasing step) algorithm and PSO (Particle Swarm Optimization) algorithm on 7 benchmark functions, DPCKH algorithm has stronger local exporatioin and exploitation ability.