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In recent years, with the development of artificial intelligence, deep learning has become one of the most important computing loads today. The next generation of artificial intelligence(AI)and high-performance computing applications have put unprecedented demands on the computing power and communication capabilities of computing platforms. Wafer-scale chips integrate ultra-high-density transistors and interconnect communication capabilities on the entire wafer, so it is expected to provide revolutionary computing power solutions for future AI and supercomputing platforms. Among them, the huge computing resources and unique new architecture of wafer-scale chips pose unprecedented challenges to task mapping algorithms. Related research has become a major focus of academic research in recent years. This paper focuses on studying the mapping methods of AI tasks on wafer-scale hardware resources. By expressing the AI algorithm as multiple convolutional kernels and considering the computational power characteristics of convolutional kernels, a mapping algorithm for wafer-scale chips is designed based on genetic algorithms. The simulation results under a series of mapping tasks verifies the effectiveness of the mapping algorithm and revealed the impact of parameters such as execution time and adapter cost on the cost function. [ABSTRACT FROM AUTHOR]

The traditional express terminal distribution mode has problems such as redundant construction of express outlets and overlapping delivery paths, and the joint distribution model can effectively solve these problems. Therefore, this paper studies the location path of express terminal outlets in the case of simultaneous receiving and dispatching and uncertain receiving demand under the joint distribution model. Firstly, a two-stage mathematical optimization model is established to deal with the problem of uncertain receipt volume by introducing random chance constraints. Secondly, a hybrid heuristic algorithm based on genetic algorithm and adaptive large neighborhood search algorithm is designed. Finally, numerical experiments show that the designed hybrid algorithm has a faster convergence speed and better solution quality than the traditional genetic algorithm. Too high or low risk acceptance of the optimization scheme in the random demand environment will lead to the increase of cost. With the increase of the ratio of customer receiving and dispatching volume, the cost of express terminal distribution first decreases and then increases, The nearest outlet return strategy can effectively reduce the distribution cost of enterprises. [ABSTRACT FROM AUTHOR]

Most of the existing trajectory privacy protection technologies protect the static trajectory data of mobile objects, but ignore the risk of privacy disclosure of the dynamic trajectory of mobile objects. In order to solve this problem, this paper studies the dynamic trajectory anonymity based on genetic algorithm. The proposed algorithm uses the characteristics of genetic algorithm to search the global optimal solution, establishes the track behavior mode in the current historical track of the moving object, forecasts the track of the moving object through the track behavior mode, and constantly updates the track behavior mode according to the new predicted track of the moving object, so as to achieve higher accuracy of track prediction. In order to protect the privacy information of the mobile object, K -anonymity technology is used to generate the false trajectory for the new prediction trajectory. Experiments show that, compared with the existing track anonymity algorithm, the proposed algorithm can protect the privacy of the track and further improve the quality of the track data. [ABSTRACT FROM AUTHOR]

The data in the outlier detection problem can be considered as the mixture of normal and abnormal points in a space. Under the premise of reducing the loss of normal points outliers are usually contained in the sample sets farthest from all clustering centroids. Inspired by this idea this paper proposes an interpolation-based outlier detection method for sparse high-dimensional data. This method interpolates the original data by applying genetic algorithm on the basis of k-means clustering solving the problem that sparse data in k-means clustering is easy to be merged. Experimental results show that compared with traditional outlier detection methods based on k-means clustering and several typical detection methods based on improved k-means clustering，the proposed method can not only lose fewer normal points but also improve the accuracy and precision of detection. [ABSTRACT FROM AUTHOR]

Spectral community detection algorithms generally divide the network via structure which is often limited by the number of divisions and it is difficult to control the degree of overlapping. This paper designs an overlapping multiway spectral community detection algorithm for attribute network which can divide the attribute network into any number of overlapping communities and effectively discover outliers. Firstly，the partition mapping method from maximization to node vectorization is designed based on the weighted modularity. Secondly the initial selection strategy of cluster center vectors is given and merged in the attributed network. Thirdly the node allocation strategy is designed to calculate the inner product of the node and clustering center vector and to assign the node to the community with the highest inner product. Finally，the tightly structured overlapping communities that have outliers are effectively detected. In addition applying the algorithm to multiple networks in the real world verifies the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

From the perspective of engineering application, LoRa wireless transmission system is required to have the characteristics of low system power consumption, short transmission distance and good system stability. Therefore, optimizing the matching parameters in its design is the key way to improve LoRa transmission performance. In this paper, aiming to achieve the lowest energy consumption, the longest transmission distance and the strongest system robustness of the LoRa wireless communication, the effective values of parameters such as SF, BW and CR are considered as the constraint condition, and the linear weighting method is adopted to convert the multi-objective optimization problem to a single-objective problem so as to work out the optimal solution. The simulation and actual test results show that the genetic algorithm applied to LoRa parameter matching is feasible and effective. [ABSTRACT FROM AUTHOR]

The fourth party logistics services are able to combine excellent logistics services through large-scale integration of resources, and provide the best logistics services scheme for the user through combination of technology, warehousing, transportation service, thus realizing the logistics service with high efficiency, low cost and real-time operation. However, how to dynamically construct reliable composite logistics service that can satisfy users' quality requirements is the critical issue for the fourth party logistics service provider to solve. To address this issue, the paper proposes a dynamic composition method of context-aware logistics Web service. Firstly, this method decomposes the global QoS constraints into local QoS constraints based on the improved genetic algorithm, and transforms the global optimization problem into a local optimal service selection problem. Secondly, during the execution of the service process, the best logistic Web service that is available and can satisfy the local QoS constraints is selected. Finally, the effectiveness and feasibility of the method are validated by experiments. [ABSTRACT FROM AUTHOR]

The existing P2P network service discovery algorithms are lack of service quality assurance consideration, and cannot fully utilize the specific service quality attributes of some nodes in the networks. In order to improve the efficiency of P2P networks service discovery, this paper proposed a P2P service discovery algorithm based on QoS by combining QoS attributes and P2P characteristics. we first defined description of P2P service based on QoS and introduces semantic information into the service description in order to improve the accuracy of service matching. On this basis, we built up the P2P service discovery model with QoS guarantees and provided a genetic algorithm for P2P service discovery. At last, the feasibility and effectiveness of the service discovery algorithm was performed through the experiments. [ABSTRACT FROM AUTHOR]

Nurse rostering problem is one of multi-constrained problems with many traps. Therefore nether traditional approaches nor heuristic methods can find the optimal solution for the problem. This paper studied and solved the nurse rostering problem by applying a hybrid method that combines genetic algorithm (GA) and variable neighborhood search (VNS). In this method, GA generates a new nurse roster by adding judgment rules and control policies. VNS algorithm operates on the steps of initializaion, crossover and mutation wtth constraints, and separation of solution space. Finally, the experiment results of 20 nurse rostering proWerns indicate that, within the same runtime, the method of GA + VNS outperforms the method of IP + VNS which is internationally known as the newest approach. [ABSTRACT FROM AUTHOR]