International Journal of Modeling, Simulation, and Scientific Computing

In the decision-making tasks for ranking the alternatives and choosing the best ones, procedures on the digraphs are often used. A digraph of the aggregated relation on a set of alternatives for information of experts or criteria is preconstructed. If the digraph does not contain any cycles, then the Demukron algorithm for partitioning the digraph into levels can be used to order the alternatives by preference. This algorithm cannot be applied if there are clusters consisting of equivalent alternatives. In the paper the algorithm for partitioning an arbitrary digraph of into preference levels is proposed. In contrast to the standard procedure, the digraph of the aggregated relation admits the presence of cycles, and, consequently, of equivalent vertexes-alternatives. The vertexes in any cycle of digraph belong to one level of preference.

In this paper, we present a new approach for complex system design, which allows rapid, efficient and low-cost prototyping. This approach can simplify designing tasks and go faster from system modeling to effective hardware implementation. Designing multi-domain systems requires different engineering competences and several tools, our approach gives a unique design environment, based on the use of VHDL-AMS modeling language and FPGA device within the same design tool. This approach is intended to enhance hardware-in-the-loop (HIL) practices with a more realistic simulation which improve the verification process in the system design flow. This paper describes the implementation of a software/hardware platform as a practical support for our approach, the feasibility and the benefits of this approach are demonstrated through a practical case study for power converter control. The obtained results show that the developed method achieves significant speed-up compared with conventional simulation, with a minimum used resources and minimum latency.

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

The occurrence of traffic accidents is regular in probability distribution. Using big data mining method to predict traffic accidents is conducive to taking measures to prevent or reduce traffic accidents in advance. In recent years, prediction methods of traffic accidents used by researchers have some problems, such as low calculation accuracy. Therefore, a prediction model of traffic accidents based on joint probability density feature extraction of big data is proposed in this paper. First, a function of big data joint probability distribution for traffic accidents is established. Second, establishing big data distributed database model of traffic accidents with the statistical analysis method in order to mine the association rules characteristic quantity reflecting the law of traffic accidents, and then extracting the joint probability density feature of big data for traffic accident probability distribution. According to the result of feature extraction, adaptive functional and directivity are predicted, and then the regularity prediction of traffic accidents is realized based on the result of association directional clustering, so as to optimize the design of the prediction model of traffic accidents based on big data. Simulation results show that in predicting traffic accidents, the model in this paper has advantages of relatively high accuracy, relatively good confidence and stable prediction result.

Multiple optimization objectives are often taken into account during the process of sensor deployment. Aiming at the problem of multi-sensor deployment in complex environment, a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed. First, the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity, and a truncation probability sensing model is presented. Two strategies, the local mutation operation and parameter adaptive operation, are introduced to improve the optimization ability of quantum particle swarm optimization (QPSO) algorithm, and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front. Then, considering the multi-objective deployment requirements, a novel multi-sensor deployment method based on the multi-objective optimization theory is built. Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once. Compared with the traditional algorithms, the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.

Optimal weights are usually obtained in neural network through a fixed network conformation, which affects the practicality of the network. Aiming at the shortage of conformation design and weight training algorithm in neural network application, the back propagation (BP) neural network learning algorithm combined with simulated annealing genetic algorithm (SAGA) is put forward. The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations. The simulated annealing mechanism is incorporated into the Genetic Algorithm (GA) to optimize the design and optimization of neural network conformation and network weights simultaneously. The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process, also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon. The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm. The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.

From 21st century, it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly. Thus, cloud computing technology with relatively low cost of hardware facilities is created. However, to guarantee the quality of service in the situation of the rapid growth of data volume, the energy consumption cost of cloud computing begins to exceed the hardware cost. In order to solve the problems mentioned above, this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment, introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software, which was compared with the performance of two algorithms, resource management (RM) and genetic algorithm (GA). The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies, and there were fewer failed virtual machines under the same number of requests, which meant that the service quality was higher.

This paper presents an approximate solution of nonlinear fractional differential equations (FDEs) that exhibit an oscillatory behavior by using a metaheuristic technique. The solutions of the governing equations are approximated by using homotopy perturbation method (HPM) along with the fractional derivative in the Caputo sense. The designed methodology is based on a weighted series of HPM in conjunction with a nature-inspired algorithm. The idea is instantly fascinated by the researchers on the consequent implementation of nature-inspired learning algorithms such as a Cuckoo search algorithm (CSA). The usage of CSA has accelerated the minimized search path of error to the convergent values of the solution. The validity and accuracy of the proposed technique are ascertained by calculating the approximate solution and the error norms which ensure the convergence of the approximation that can be further increased. The critical analysis is also provided by the numerical simulation of two different test models. Discussion of key points has been determined by the tabulation of numerical values and graphs. Comparative study of the results with known numerical technique is also performed.