The variable-length address is one of the important research content in the field of future network. Aiming at the low efficiency of traditional routing lookup algorithms for variable-length address， an efficient routing lookup algorithm suitable for variable-length addresses based on balanced binary tree — AVL （Adelson-Velskii and Landis） tree and Bloom filter， namely AVL-Bloom algorithm， was proposed. Firstly， multiple off-chip hash tables were used to separately store route entries with the same number of prefix bits and their next-hop information in view of the flexible and unbounded characteristics of the variable-length address. Meanwhile， the on-chip Bloom filter was utilized for speeding up the search for route prefixes that were likely to match. Secondly， in order to solve the problem that the routing lookup algorithms based on hash technology need multiple hash comparisons when searching for the route with the longest prefix， the AVL tree technology was introduced， that was， the Bloom filter and hash table of each group of route prefix set were organized through AVL tree， so as to optimize the query order of route prefix length and reduce the number of hash calculations and then decrease the search time. Finally， comparative experiments of the proposed algorithm with the traditional routing lookup algorithms such as METrie （Multi-Entrance-Trie） and COBF （Controlled prefix and One-hashing Bloom Filter） were conducted on three different variable-length address datasets. Experimental results show that the search speed of AVL-Bloom algorithm is significantly faster than those of METrie and COBF algorithms， and the query time is reduced by nearly 83% and 64% respectively. At the same time， AVL-Bloom algorithm can maintain stable search performance under the condition of large change in routing table entries， and is suitable for routing lookup and forwarding with variable-length addresses.

Graph matching is widely used in reality, of which subgraph isomorphic matching is a research hotspot and has important scientific significance and practical value. Most existing subgraph isomorphism algorithms build constraints based on neighbor relationships, ignoring the local neighborhood information of nodes. In order to solve the problem, a subgraph isomorphism matching algorithm based on neighbor information aggregation was proposed. Firstly, the aggregated local neighborhood information of the nodes was obtained by importing the graph attributes and structure into the improved graph convolutional neural network to perform the representation learning of feature vector. Then, the efficiency of the algorithm was improved by optimizing the matching order according to the characteristics such as the label and degree of the graph. Finally, the Constraint Satisfaction Problem (CSP) model of subgraph isomorphism was established by combining the obtained feature vector and the optimized matching order with the search algorithm, and the model was solved by using the CSP backtracking algorithm. Experimental results show that the proposed algorithm significantly improves the solving efficiency of subgraph isomorphism compared with the traditional tree search algorithm and constraint solving algorithm.

Large amount of uncertainty in PPI network and the incompleteness of the known protein complex data add inaccuracy to the methods only considering the topological structural information to search or performing supervised learning to the known complex data. In order to solve the problem, a search method called XGBoost model for Predicting protein complex (XGBP) was proposed. Firstly, feature extraction was performed based on the topological structural information of complexes. Then, the extracted features were trained by XGBoost model. Finally, a mapping relationship between features and protein complexes was constructed by combining topological structural information and supervised learning method, in order to improve the accuracy of protein complex prediction. Comparisons were performed with eight popular unsupervised algorithms: Markov CLustering (MCL), Clustering based on Maximal Clique (CMC), Core-Attachment based method (COACH), Fast Hierarchical clustering algorithm for functional modules discovery in Protein Interaction (HC-PIN), Cluster with Overlapping Neighborhood Expansion (ClusterONE), Molecular COmplex DEtection (MCODE), Detecting Complex based on Uncertain graph model (DCU), Weighted COACH （WCOACH）； and three supervisedmethods Bayesian Network (BN), Support Vector Machine (SVM), Regression Model (RM). The results show that the proposed algorithm has good performance in terms of precision, sensitivity and F-measure.

Focusing on the reconnoitering problem of Linear Frequency Modulation (LFM) pulse signals, a method to estimate the whole parameters containing frequency modulate rate, center frequency, time of arrival and pulse width, was proposed. Firstly, frequency modulate rate as well as time-frequency relation was estimated based on Fractional Fourier Transform (FrFT), then partial correlation pulses was used for signal accumulation, at last the autocorrelation technology was used to estimate the center frequency, time of arrival and pulse width. The Cramer-Rao Low Bounds (CRLB) for the parameters were derived and the effect on estimation error caused by signal to noise ratio was analyzed. Finally, the effect on estimation error caused by the width of partial accumulation pulse was analyzed, and some advice was given on choosing the width of accumulation pulse. Simulation results show that the estimation error of frequency modulate rate is close to CRLB. When signal to noise ratio is 0 dB without any knowledge of baseband and modulation parameters, the Root Mean Square Error (RMSE) of center frequency is about 10 ^-1MHz orders of magnitude, and the RMSE of time of arrival as well as pulse width is about 10 ^-1 μs orders of magnitude. The estimate error, which is affected by the correlation pulse width, decreases with the increase of correlation pulse width, and then increases. The proposed method is especially applicable to the reconnoitering of new system radar such as chirp radar, and Synthetic Aperture Radar (SAR).

The advantages of simplicity and easy implementation of Particle Swarm Optimization (PSO) algorithm have been validated in science and engineering fields. However, the weaknesses of PSO algorithm are the same as that of other evolutionary algorithms, such as being easy to fall into local minimum, premature convergence. The causes of these disadvantages were analyzed, and an improved algorithm named Cosine PSO (CPSO) was proposed, in which the inertia weight of the particle was nonlinearly adjusted based on cosine functions and the learning factor was symmetrically changed, as well as population diversity was maintained based on bacterial chemotaxis. Therefore, CPSO algorithm is better than the Standard PSO (SPSO) in a certain degree. Simulation comparison of the three algorithms on five standard test functions indicates that, CPSO algorithm not only jumps out of local optimum and effectively alleviates the problem of premature convergence, but also has fast convergence speed.