Aiming at the shortcomings of Equilibrium Optimizer （EO） such as low optimization accuracy， slow convergence and being easy to fall into local optimum， a new EO in consideration with distance factor and Elite Evolutionary Strategy （EES） named E-SFDBEO was proposed. Firstly， the distance factor was introduced to select the candidate solutions of the equilibrium pool， and the adaptive weight was used to balance the fitness value and distance， thereby adjusting the exploration and development capabilities of the algorithm in different iterations. Secondly， the EES was introduced to improve the convergence speed and accuracy of the algorithm by both elite natural evolution and elite random mutation. Finally， the adaptive t-distribution mutation strategy was used to perturb some individuals， and the individuals were retained with greedy strategy， so that the algorithm was able to jump out of the local optimum effectively. In the simulation experiment， the proposed algorithm was compared with 4 basic algorithms and 2 improved algorithms based on 10 benchmark test functions and Wilcoxon rank sum test was performed to the algorithms. The results show that the proposed algorithm has better convergence and higher solution accuracy.

Concerning the problem that uncertainty information is difficult to be merged during the decision-making process of multi-source irrigation information, a decision fusion method based on fuzzy rough set and Dempster-Shafer (D-S) evidence theory was proposed. Using the fuzzy rough set theory,the basic probability distribution function was established, the interdependence between irrigation factors and irrigation decision was calculated, and the identification framework of irrigation decision on the multiple fusion irrigation factors was built. Using the improved D-S evidence theory, the multi-source irrigation information was fused at the decision-making level, the expression and synthesis problems of uncertain information were solved. The information of winter wheat such as soil moisture, photosynthetic rate and stomatal conductance in north China was fused in irrigation decision by the application of the methods mentioned above. The results show that the uncertainty of the irrigation decision decreases from 38.0% before fusion to 9.84%. The method can effectively improve the accuracy of irrigation decision and reduce the uncertainty of the irrigation decision.

The fourth-party logistics company alliance building problem is to study how to build alliance with a method of high efficiency and low cost. An improved algorithm about graph clustering based on Particle Swarm Optimization (PSO) was proposed to solve the problem, and it contributed to reduce the connection cost between companies in the group. Discretization PSO was used to optimize the clustering result of graph clustering algorithm, and the result with disturbance strategies got enhanced. Clustering the 100 virtual companies, the cost was reduced from 39991 to 24800. The experimental result shows that, the improved graph clustering algorithm based on discretization PSO can solve the problem in a way with high efficiency and low cost.

Aiming at the descriptiveness and parsimony problems of historical disaster records a historical disaster classification method which based on ant colony clustering is proposed in this paper. The disaster data are normalized by using gray relational analysis approach, and then the levels of historical disasters are divided by the results of ant colony automatic clustering, so as to avoid the arbitrary man-made interference. In comparison with other classification methods in performance, experimental results show that this method has high accuracy and practicality.

To cope with the variation of network conditions in scalable video streaming, a new Adaptive Media Playout (AMP) algorithm was proposed which predicates the risk of playout outage and buffer overflow and adjusts the frame rate in advance. The algorithm estimated the throughput of network and the lengths of frames in the video’s GOP structure for risk predication, realized adjustments in K steps for good smoothness and speed, and reduced quality loss of the video by exploiting the scalability of SVC stream. The simulation results show that the proposed algorithm outperforms the existing smooth and conventional AMP algorithms in outage suppressing, overflow processing and jitter performance.

Poor generalization of feature parameters classification and large category computation reduce the classification performace of K-Nearest Neighbor (KNN). An improved KNN based on union entropy under the attribute reduction condition was proposed. Firstly, the size of classification impact of data feature was measured by calculating the union entropy of two feature parameters relative to any two condition attributes, and the intrinsic relation was established between classified features and the specific classification process. Then, the method which reduced condition attributes according feature union entropy set was given. The theoretical analysis and the simulation experiment show that compared with the classical KNN, the improved algorithm has better classification performance.

Nowadays, as low carbon economy has been advocated worldwide, the power consumption of embedded software has become a critical factor in embedded system design. The hardware/software partitioning is an important method of embedded software power optimization. Firstly, this paper constructed a hardware/software bi-partitioning model with the goal of embedded software power consumption under the constraints of performance; then, a hybrid algorithm was proposed based on the fusion of discrete Hopfield Neural Network (HNN) and Tabu Search (TS), in which HNN as the main method could quickly obtain a feasible solution of partitioning, and the TS algorithm could "taboo" the current solution and transferred to the other minimum points that could jump out from the local optimal solution. Lastly, the experimental results show that the proposed algorithm posses better time performance and higher probability of acquiring the global optimal solution in contrast with other similar algorithms.

Concerning the security of the Contourlet-based set partitioning in hierarchical trees compression method, a new image encryption algorithm based on chaos mapping was proposed. The presented algorithm contained two steps: ranked tables permutation using chaotic scrambling arrays; bit streams XOR processing using chaotic binary streams. The simulation results indicate that the cipher image satisfies requests for content masking. It also demonstrates that the proposed algorithm is sensitive to the initial values and has fast speed and high security.

A fast mode decision algorithm was presented for H.264/AVC intra prediction based on the feature of the block and neighboring block's mode information. The number of candidate mode was reduced, for 4×4 luminance block, from 9 to 1~4. Experimental results show that, compared with the H.264 encoder JM8.6, the proposed scheme reduces coding time by 50%~60% for Iframe while keeping PSNR and bit rate almost unchanged.