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Protein structure prediction is an important factor in the area of bioinformatics. Predicting the two-dimensional structure of proteins based on the hydrophobic polarity model (HP model) is a typical non-deterministic polynomial(NP)-hard problem. Currently, HP model optimization methods include the greedy algorithm, particle swarm optimization, genetic algorithm, ant colony algorithm and the Monte-Carlo simulation method. However, the robustness of these methods are not sufficient, and it is easy to fall into a local optimum. Therefore, a HP model optimization method, based on reinforcement learning was proposed. In the full state space, a reward function based on energy function was designed and a rigid overlap detection rule was introduced. By using the characteristics of the continuous Markov optimal decision and maximizing global cumulative return, the global evolutionary relationship in biological sequences was fully exploited, and effective and stable predictions were retrieved. Eight classical sequences from publications and Uniref50 were selected as experimental objects. The robustness, convergence and running time were compared with the greedy algorithm and particle swarm optimization algorithm, respectively. Both reinforcement method and swarm optimization method can find all the lowest energy structures for these eight sequences, while the greedy algorithm only detects 62.5%. Compared with particle swarm optimization, the running time of the reinforcement method is 63.9% lower than that of particle swarm optimization.

Because any MapReduce job requires a series of complex operations such as task scheduling and resource allocation independently, there are a lot of redundant disk I/O and resource duplicate application operations among multiple MapReduce jobs coordinated by the same algorithm, causing inefficient resource utilization in job computing process. Big data mining algorithms are usually divided into several MapReduce Jobs, taking ItemBased algorithm as an example, this paper has analyzed the resource efficiency of mining algorithm with multi-MapReduce job collaboration scenario. It proposed an ItemBased algorithm based on DistributedCache, which used DistributedCache to cache I/O data between multiple MapReduce Jobs, breaks the defect of independence between jobs, and reduced the waiting delay between Map and Reduce tasks. The experimental results show that, DistributedCache can improve the data reading speed of MapReduce jobs. The algorithm reconstructed by DistributedCache greatly reduces the waiting delay between Map and Reduce tasks, and improves the resource efficiency by more than three times.

In order to overcome the premature convergence of particle swarm optimization (PSO) algorithm, an improved PSO algorithm based on sub-groups mutation (SsMPSO) is proposed. This algorithm has proposed the sub-groups with random directional vibrating search to mutate the global optimal position of the main swarm and changed the way of random mutation. The mutation based on sub-groups enabled the algorithm had excellent local exploit ability and circumvented the premature convergence. It used another mutation on bad particles to enhance the algorithm’s global exploit ability and expand the searching space. Finally, high dimension benchmark functions have been used to test the performance of improved algorithm. The simulation results show that the proposed algorithm can effectively overcome the premature problem, the multimodal function optimization can avoid local extreme point and the convergence and convergence accuracy are greatly improved.

This paper studied the problem of imaging the moving target in cross range direction by using conventional narrow-band radar and indicates that the key technique to obtain the cross range profile of target is motion compensation. In this paper, after the analysis in theory, a motion compensation algorithm based on third order polynomial coefficients estimation was proposed. In order to optimize these estimating coefficients, an optimal criterion was also presented. Moreover, this paper also proposed an algorithm to estimate the cross-range resolution. Simulation results show the presented method of cross range imaging is effective and the algorithm of estimating the resolution is reasonable.

This paper presents a new method to detect the road in synthetic aperture radar (SAR) images based on B-spline wavelet. In this method, B-spline wavelet is constructed firstly. The roads can be detected by searching the maximal amplitude of the SAR images' wavelet transform. In order to alleviate the influence of the speckle of SAR image, an idea of mean was introduced into this algorithm. Experiment results shows that the algorithm is very effective and superior to other classical edge detection operators.