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.

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.

With the continuous development of space-borne remote sensing technology, the remote sensing data has become increasingly large. At present, the limited bandwidth of communication can not meet the demand of remote sensing image data transmission. Therefore, the research of image compression technology for space-borne applications is of great significance to the development of space application technology. It is difficult to meet the performance requirements by adopting traditional single-core DSP (Digital Signal Processor), and it is difficult to meet the power demand by adopting Field-Programmable Gate Array (FPGA). In recent years, with the development of hardware technology, multi-core DSP technology has matured, and there are mature multi-core DSP image compression solutions in missile-borne scene for space-borne application reference. Based on multi-core DSP, TI's C6678 multi-core floating-point DSP platform, this paper constructed a parallel image compression system and made full use of the hardware resources of multi-core DSP. Considering the compression of space-borne remote sensing image has a high demand on compression quality, compression speed and other indicators, the system took the JPEG2000 standard as the image compression standard, using the main core responsible for external communication and internal task allocation, and using slave core to implement JPEG2000 image compression of the design. Test results show that the system is stable and reliable, and the overall compression performance is excellent, to meet the performance requirements of space-borne remote sensing image compression system.

A reflex workflow system model was proposed. The model could monitor the system change automaticly without help of people, and modify the content of running business process dynamically. So the business process can dynamically adapt to the circumstance by changing it own content to finish business target. By the reflexive model, workflow system has the flex ability to changing circumstance.

Workflow engine is the core component of workflow system, architecture of which is vital to the whole system. This paper addressed the architecture of J2EE-based workflow engine, which was consisted of six components: parser, process manager, executor, task assigner, event server, time server and client interface. By the comparisons with other similar research works, the advantages of this approach in terms of applicability, expansibility, interview mode and transaction management were illustrated.