For Mixed-Variable Optimization Problem (MVOP) containing both continuous and categorical variables, a coevolution strategy was proposed to search the mixed-variable decision space, and a Coevolutionary Ant Colony Optimization Algorithm for MVOP (CACOA _MV) was developed. In CACOA _MV, the continuous and categorical sub-populations were generated by using the continuous and discrete Ant Colony Optimization (ACO) strategies respectively, the sub-vectors of continuous and categorical variables were evaluated with the help of cooperators, and the continuous and categorical sub-populations were respectively updated to realize the efficient coevolutionary search in the mixed-variable decision space. Furthermore, the ability of global exploration to the categorical variable solution space was improved by introducing a smoothing mechanism of pheromone, and a "best+random cooperators" restart strategy facing the coevolution framework was proposed to enhance the efficiency of coevolutionary search. By comparing with the Mixed-Variable Ant Colony Optimization (ACO _MV) algorithm and the Success History-based Adaptive Differential Evolution algorithm with linear population size reduction and Ant Colony Optimization (L-SHADE _ACO), it is demonstrated that CACOA _MV is able to perform better local exploitation, so as to improve approximation quality of the final results in the target space; the comparison with the set-based Differential Evolution algorithm with Mixed-Variables (DE _MV) shows that CACOA _MV is able to better approximate the global optimal solutions in the decision space and has better global exploration ability. In conclusion, CACOA _MV with the coevolutionary strategy can keep a balance between global exploration and local exploitation, which results in better optimization ability.

Concerning the problem of high energy consumption caused by data collision and cluster selection process in dynamic clustering target tracking of Wireless Sensor Network (WSN), a dynamic clustering method based on energy optimization for WSN was proposed. Firstly, a time division election transmission model was proposed, which avoided data collision actively to reduce energy consumption of nodes in a dynamic cluster. Secondly, based on energy information and tracking quality, the energy-balanced farthest node scheduling strategy was proposed, which optimized custer head node scheduling. Finally, according to the weighted centroid localization algorithm, the target tracking task was completed. Under the environment of random deployment of nodes, the experimental results show that, the average tracking accuracy of the proposed method for non-linear moving objects was 0.65 m, which is equivalent to that of Dynamic Cluster Member Selection method for multi-target tracking (DCMS), and improved by 45.8% compared to Distributed Event Localization and Tracking Algorithm (DELTA). Compared with DCMS and DELTA, the proposed algorithm can effectively reduce energy consumption of the dynamic tracking clusters by 61.1% and prolong the network lifetime.

Concerning the problem that there is a lot of data which need to be real-time processed during the production process, the local processor, based on multi-thread and co-processing architecture and two data buffer mechanisms was accomplished. As a reference, multi-functional thread in Hadoop's parallel architecture has an impressed impact on the design of the local processor, especially MapReduce principle. Based on the user-defined architecture, the local processor ensures data concurrency and correctness during receiving, computing and uploading. The system has been put into production for over one year. It can meet the enterprise requirements and has good stability, real-time, effectiveness and scalablility. The application result shows that the local processor can achieve synchronized analysis and processing of mass data.