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Abstract-Objective: A novel Greedy Randomized Adaptive Search Procedure was proposed in this paper to resolve the traveling salesman problem, which is proven to be NPcomplete in most cases.Methods: The proposed novel algorithm has two phases. In the first phase the novel algorithm finds an initial solution of the problem with a proposed mergence feature greedy randomized method. In the second phase the expanded neighborhood adaptive search procedure was proposed to find the TSP solution.Results: The proposed algorithm was tested on numerous benchmark problems from TSPLIB. The algorithm is compared with other two algorithms and the results showed that the results of the proposed algorithm are always the best. The results were very satisfactory.Conclusion: For the majority of the instances the results were equal to the best known solution. The algorithm is suitable for the TSP. This kind of novel algorithm can be used for many aspects of object, especially for logistical problem.

Network motifs are subnetworks that appear in the network far more frequently than in randomized networks. They have gathered much attention for uncovering structural design principles of complex networks. One of the previous approaches for motif detection is sampling method, in- troduced to perform the computational challenging task. However, it suffers from sampling bias and probability assignment. In addition, subgraph search, being very time-consuming, is a critical process in motif detection as we need to enumerate subgraphs of given sizes in the original input graph and an ensemble of random generated graphs. Therefore, we present a Degree-based Sampling Method with Partition-based Subgraph Finder for larger motif detection. Inspired by the intrinsic feature of real biological networks, Degree-based Sampling is a new solution for probability assignment based on degree. And, Partition-based Subgraph Finder takes its inspiration from the idea of partition, which improves computational efficiency and lowers space consumption. Experimental study on UETZ and E.COLI data set shows that the proposed method achieves more accuracy and efficiency than previous methods and scales better with increasing subgraph size.

In recent years, with the popularization of electronic toll collection systems (ETC), expressway toll stations in China have developed into a mixed toll form jointly deployed by ETC toll lanes and MTC toll lanes, presenting a development trend based on ETC toll lanes. Since the toll collection system has the important function of “credit repayment“, the relationship between operation benefit and cost will become an important indicator for measuring the toll station system. Taking the minimum cost and the maximum benefit of the toll station under normal operating condition as the optimization goals, a multi-objective nonlinear optimization model is established within the seek for balance between cost and benefit, so as to obtain an open plan with better toll lanes. The Nanjing-Hangzhou toll station is used as an example to verify the model. Furthermore, based on the trend of the future traffic flow and the increase in the proportion of ETC vehicles, the distribution law of the optimal opening schemes of the toll lane under different traffic volumes and different traffic flow structures is analyzed, which provides experience support for the construction and operation of toll stations in the future.