With the development of Internet of Things (IoT) technology,its application scenarios have exploded recently,and such applications are generally delay-sensitive and resource-constrained.It is a focused issue in the way of offloading the real-time tasks under the condition of limited resource.Besides,it is a NP-hard combinatorial optimization problem to allocate limited computational resources for the real-time tasks.To solve this problem,this paper proposes a real-time resource management algorithm based on Lyapunov optimization,aiming at stabilizing the virtual queues while optimizing the total power consumption and total utility.Firstly,the optimization model for the total power consumption and weighted total utility is proposed under the constraint of computation and communication resources.This model contains of two virtual buffer queues,and tasks are unloaded in a device-to-device (D2D) scheduling model.Then,an optimization algorithm is proposed based on Lyapunov optimization to decompose the joint long-term average sum energy consumption and sum utility optimization problem into a series of real-time optimization problems.To solve these problems,a greedy-based matching algorithm is proposed.Experimental results demonstrate that the performance of the proposed algorithm is 8.6% better than the best result of random method and can approximate the exhaustive attack method under different connection degrees.

There are certain vulnerabilities at the beginning of multiple domain cyberspace configuration. How to discover these potential vulnerabilities has been a hot topic. This paper proposes to find these cyberspace vulnerabilities by discovering the shortest attack path in multiple domain cyberspace. In order to discover more and shorter attack paths, we train an agent as an attacker to discover multiple domain cyberspace attack paths. We formulate the discovering attack paths as a reinforcement learning (RL) problem. With this technique, we added a multiple domain action select module to RL that can pick an executable action in a state. By using the proposed method, we can discover more hidden attack paths and shorter attack paths to analyze the potential vulnerabilities to cyberspace. Finally, we created a simulated cyberspace experimental environment to test our proposed method. The experimental results show that the proposed method can discover more hidden multiple domain attack paths and shorter attack paths than the existing methods.

针对传统开发者推荐方法侧重于分析开发者的专业能力以及与任务的交互信息,而未考虑开发者间协作性的问题,提出一种基于E-CARGO(Environment-Class,Agent,Role,Group,and Object)模型的开发者推荐方法.首先结合协作开发的特点,将开发者协同开发过程描述为基于角色的协作,并使用E-CARGO模型对其进行建模;然后使用模糊层次分析(FAHP)法来建立模糊判断矩阵,从而求得开发者能力指标权重并进行加权求和,进而得到开发者历史综合能力评价集合;最后针对开发者综合能力评价的不确定和动态特性,利用云模型理论对开发者历史综合能力评价集合进行分析,以求得开发者对于每个任务的胜任能力,并利用cplex优化包对开发者推荐问题进行求解.实验结果表明,所提方法能够在可接受的时间范围内求得最优的开发者推荐结果,验证了所提方法的有效性.

边缘计算作为一种新模式,可以有效解决附近移动设备运算能力不足的问题.然而,由于现实世界中复杂的状况,各个时间段内用户的位置分布难以预测,由配备有限资源的边缘服务器覆盖的区域内将难以承载各时间段内不均衡不恒定数量的用户,导致用户无法请求服务.另外,不合理的分配策略将降低区域内承载用户的能力,并可能造成资源浪费.针对上述问题,提出一种自适应移动路径感知的用户分配算法.该算法首先利用用户的位置信息和路网数据,通过改进的地图匹配方法确定用户行进状态,并对用户未来移动路径进行预期;然后,基于用户预期路径提出一种分配策略,以服务器范围内用户预期停留时长作为分配策略的适应值,确保用户拥有更久的稳定连接以及更少的因超出信号范围而丢失连接;最后,提出一种基于最佳适应(Best-Fit)的分配策略重构方法,通过将满载服务器中部分用户迁移至邻近有空闲空间的服务器,间接增加了区域内服务器的总用户容量,降低了周边服务器的空闲时间,从而提升了资源利用率.通过基于真实用户轨迹数据集的对比实验表明,本算法在用户覆盖率及资源利用率上明显优于现有算法.

On the premise of analyzing various evaluation methods and means and constructs the comprehensive evaluation model of external knowledge transfers risk of transformation enterprises. BP network has robustness and fault tolerance, it is distributed storage of information, local damage will be a certain degree of reduce the network performance, does not result in a catastrophic risk, tolerance with a larger error in the input value, and even individual errors exist, at the same time each activity unit has the independence of the information processing, which can realize the parallel computing, improve speed.

Feature selection is an important pre-processing in data mining, due to a lot of redundant and irrelevant features in datasets. A filter-wrapper multi-objective feature selection method based on hybrid mutual information and particle swarm optimization algorithm (HMIPSO) is proposed. Based on the number of iterations of the pbest of the particle from the last update, an adaptive mutation strategy is proposed to disturb the population and avoid the population falling into local optimum. Meanwhile, a new set concept based on the Pareto front and archive is proposed. Combining mutual information and the new set knowledge, a local search strategy is proposed, which enables particles in Pareto front to delete irrelevant and redundant features, and then the Pareto front before and after learning is updated by Elite. Finally, this paper compares the effectiveness of HMIPSO with other 4 multi- objective algorithms on 15 UCI datasets. The experimental results show that HMIPSO can reduce the number of features and classification error rate efficiently.

Feature selection is an important step of data preprocessing in data mining, so selecting the optimal feature subset can reduce the data dimension and computing cost of learning algorithm effectively. In this paper, binary particle swarm optimization (BPSO) is used to optimize the feature selection process. A strategy of population initialization based on feature clustering information is proposed, in which feature clustering is completed by community partitioning algorithm. According to the partitioned information, information redundancy is reduced and the quality of initial population is improved. Then, an adaptive local search strategy based on decision space similarity is proposed, in which the similarity index of particles is determined by the similarity of particles in decision space. In the evolutionary process, the particles are adaptively adjusted for local search to avoid premature algorithm. Finally, 3 representative optimization algorithms are selected to carry out comparative experiments on 11 UCI datasets. The experimental results show that the improved BPSO algorithm has a better performance in reducing the number of features than other comparison algorithms, and the classification accuracy is also significantly improved.

In classification problem, there are a large number of redundant and irrelevant features in datasets which not only can’t increase the accuracy of classification, but also reduce the speed of the classification algorithm. Feature selection can solve these problems by maximizing the classification accuracy and minimizing the number of features, and it can be regarded as a multi-objective optimization problem because these are two contradictory objects. In order to enhance the efficiency of feature selection, an improved multi-objective artificial bee colony algorithm based on Knee Points for feature selection is proposed in this paper. A method for fast recognizing Knee Points is designed to improve the employed and onlooker bee phase. Experiment results on feature selection on 11 UCI datasets with 3 other traditional multi-objective algorithms show that the algorithm proposed in this paper has significant effect in reducing the number of classification and increasing the accuracy of classification.

The traditional seismic data anomaly identification method usually adopts a simple threshold method to determine the data collected by the device outside the set normal monitoring threshold as abnormal data, and the abnormal data cannot be accurately identified. This paper adopts a method to improve the traditional anomaly recognition L-IAZPSO-SVM method to improve the accuracy of abnormal data recognition.In this paper, the earthquake precursor data [1] provided by China Seismic Network is used as the input index, and the improved PSO (Particle Swarm Optimization) [2] is used to optimize the SVM(support vector machine) [3] training to realize the identification and prediction of abnormal data. First Using Cluster analysis and PCA(Principal Component Analysis) [4] make the data preprocessed, and then Itô Lemma [5] is used to optimize the PSO initialization process to improve the particle variability and make the initial particles diverse. The adaptive speed update method is used to improve the problem that the algorithm is easy to fall into the local optimal solution in the later stage of the algorithm. The Ziggurat algorithm [6] is used to increase the number of particles and solve the problem of imbalance between the global search capability and the local search capability of the system. The improved algorithm solves the problem that the traditional SVM method cannot determine the number of support vectors, the system fitting fuzzy problem and improves the algorithm's abnormal recognition rate and prediction rate. Compared with the traditional PSO-SVM method, other improved PSO-SVM methods, and other anomaly data detection algorithms, the system's running speed, global search capability, operational stability, and abnormal data recognition rate prediction rate are improved.

The area under receiver operating characteristic curve (AUC) is one of the widely used metrics for measuring imbalanced data classification results. Designing multi-objective evolutionary algorithms for AUC maximization problem has attracted much attention of researchers recently. However, most of these methods either search the Pareto front directly, or perform tailored convex hull search for AUC maximization. None of them take the advantage of multi-level knee points found in the process of evolution for AUC maximization. To this end, this paper proposes a multi-level knee point based multi-objective evolutionary algorithm (named MKnEA-AUC) for AUC maximization on the basis of a recently developed knee point driven evolutionary algorithm for multi/many-objective optimization. In MKnEA-AUC, an adaptive clustering strategy is proposed for automatically determining the knee points on the current population. By utilizing the preference of found knee points, the evolution of the population can converge quickly. We verify the effectiveness of the proposed algorithm MKnEA-AUC on 13 widely used benchmark data sets and the experimental results demonstrate that MKnEA-AUC is superior over the state-of-the-art algorithms for AUC maximization.

The fourth Huaian pumping station is one of the second-stage pumping stations on the east route of the south-to-north water diversion project in China. The operation optimization problem of three pumps in the station is formulated to minimize the electricity cost while satisfying the flow demand. After analyzing the characteristics of the problem, a decomposition method is proposed to reduce the dimensionality of the optimization problem and thus the computation time. Simulation shows that the energy cost is reduced by 2.54% compared with the benchmark scheduling based on the proposed method. In comparison with the decomposition/aggregation-dynamic programming method and the dynamic programming with successive approximation method, the proposed algorithm can effectively save electricity costs for Huaian pumping station. The case study shows that the cost efficiency comes from two aspects: demand shift from the time intervals with a high electricity price to those with a low electricity price, and the operation mode with high energy efficiency. The former is subject to the pumps' capacity and the daily demand. The larger the pumps' capacity is, the more demand can be shifted and thus the lower the cost is. The latter is subject to the pumps' characteristics and the pump heads. The larger the head is, the smaller the difference for energy efficiencies with different blade angles are, and the smaller the energy savings with the optimal operation are. When the demand is high for a given pump head, demand shifting is the main reason, while the second aspect is the main reason when the demand is low.

There exist some problems that the traditional Artificial Bee Colony algorithm(ABC)and its extension in multi object(MOABC)has a slow convergence speed,easily falling into local minima,optimization accuracy lost and other issues under the condition of high dimension,multi peak function.Based on the characteristic of knee points that it can improve convergence and distribution,an algorithm that rapidly identificates the knee points is designed in this paper and applied to the MOABC,it proposes the improved Multi-Objective Artificial Bee Colony algorithm based on the strategy of Knee point(KnMOABC).In the iterative process,the pareto dominating relation is taken into account firstly,and the knee points are selected as the individuals for next generation,which greatly enhances the convergence speed of the algo-rithm,at the same time,an adaptive strategy is added into the knee point recognition algorithm to ensure the distributivity of the algorithm.The experimental results show that the performance of KnMOABC is better than that of the three latest multi-objective artificial bee colony algorithm.

In the era of big data,the service composition problem has attracted great attention. However,with the number of web serv-ices increasing,how to find the service composition under the dynamic environment to meet the needs of users has become an unsolved problem. An improved particle swarm optimization algorithm is proposed to solve the problem of large scale service composition opti-mization in big data environment,which effectively overcome the shortcomings appeared in traditional particle optimization algorithm, such as low diversity of discrete optimization problems and easily falling into local optimization. Firstly, particle activity detection mechanism is adopted to increase the diversity of the population in this paper,so as to effectively enhance the diversity of service com-position optimization scheme. Then,this paper introduced firework explosion mechanism to improve searching ability of particles in the process of particle learning. Meanwhile,this paper introduced particle reverse-learning theory to significantly improve ability of service composition global optimization. Experiments show that the improved algorithm in this paper can solve the service composition prob-lem of different scale,and has stronger optimal performance.

To address the problem of minimizing the total weighted completion time on parallel batch processing machines with identical machine capacities, non-identical job sizes and unequal weights, an effective meta-heuristic based on ant colony optimization is proposed. After presenting a mathematic model of the problem, we provide an algorithm to calculate the lower bound. Then, a meta-heuristic is proposed to solve the problem. The heuristic information is defined with consideration of job weights and job sizes. Meanwhile, a candidate set for constructing the solution is used to narrow the search space. Additionally, to improve the solution quality, a local optimization strategy is incorporated. Simulation results show that the proposed algorithm is able to obtain a high-quality solution within a reasonable time, and outperforms the compared algorithms.

In the practical engineering optimization problems,most of the problems are multi-objective optimization prob-lem,multi-objective optimization problem is a research hotspot for a long time.This paper proposes a new fruit fly optimi-zation algorithm,and successfully uses the algorithm to solve a multi-objective knapsack problem.The improved fruit fly algorithm uses clustering strategy and dynamic radius.In group A,a new feasible solution is explored from the position of population in the dynamic radius.In group B,it uses the crossover operation between the non dominated individuals.The initial location of the fly population is selected by random in the non dominated solutions.The convergence speed of the algorithm is improved greatly.By testing in multiple data sets with PSO and NSGA-2,the final results show that using the proposed algorithm under certain conditions can take the better search result,which proves the feasibility of using fruit fly optimization algorithm to solve the multi-objective problem.

为了克服原始萤火虫算法(Glowworm swarm optimization,GSO)对于高维、多峰函数寻优精度不高和后期收敛速度较慢的问题.为此,有针对性地提出了一种改进的动态步长萤火虫优化算法,在整个迭代期间,萤火虫的算法步长呈非线性递减.在寻优初期保持着一个相对较大的步长进行搜索,增强其全局寻优能力,在算法后期保持一个较小的移动步长,增强其局部搜索能力.此外将原始萤火虫算法在Nit集合为0时不移动,改成试探性向外随机移动策略.采用该算法的改进思想,能在一定的程度上避免算法因为过早的成熟而陷入局部最优,并且改进的算法比原始萤火虫算法有着更好的收敛精度.通过与原始GSO和改进算法ASGSO做对比,Matlab实验仿真表明,此改进算法在一定程度上提高了收敛速度和寻优精度.

针对由测距误差造成的无线传感器网络节点定位精度较低问题,提出一种基于果蝇优化算法的无线传感器网络节点定位方法.该方法将节点定位问题转化为约束优化问题进行求解,在求解的过程中利用动态步长机制来控制果蝇种群的寻优范围.当果蝇算法收敛时,在最优解附近根据测距误差的大小进行震荡寻优,对寻优产生的果蝇种群进行质心定位得出最终的定位结果.仿真实验表明,该方法收敛速度快,定位精度高,具有较强的抗误差能力.

There are many MINLP problems in the design of science and engineering systems, which are rich in variables and have many constraints and are difficult to solve.Therefore, this paper proposes an improved fruit flies algorithm.The algorithm uses different strategies to update different types of variables, and uses the periodic step function to guide the optimization of FOA so as to avoid falling into local optimization.Compared with the other two commonly used algorithms in terms of stability, convergence speed and so on, experimental results show that the improved fruit flies algorithm can effectively solve the MINLP problems.

For the traditional fruit flies optimization algorithm has the disadvantage of low optimization accuracy and easily falls into local minimum point, this paper proposes a group search strategy which has different radii of flight to greatly increase the population diversity of fruit flies in the search region. In the fruit flies individual flight distance and step func-tion, it introduces different functions into different fruit flies subgroups. Those functions have cycle oscillation properties. It can well avoid flies group into local minimum point to get the optimal solution. Through the simulation experiment for the eight test functions, it verifies these strategies can effectively improve the search precision, convergence speed and stability.

本文在传统的果蝇优化算法基础上提出了一种基于动态线性步长的分群交替优化算法.首先利用动态线性步长来控制算法搜索空间的大小,从而平衡了算法寻优的全局性能和局部性能.其次,利用双子群交替策略来解决多峰优化函数容易陷入局部最优的问题.最后,使用了6个经典测试函数来进行实验测试,验证了本文提出的算法具有求值精度高、稳定和收敛速度快等优点.