Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm

Test paper generation is a multi-objective parameter optimization problem under multi-constraints. Aiming at the defects of test paper quality and speed in intelligent test paper generation, the genetic algorithm is improved on the basis of the original genetic algorithm. From the coding method of the test questions, the establishment of weighted objective function to optimize the fitness function, the selection of crossover operator, the setting of mutation, and the combination of optimization strategy and roulette to select the operation, the optimization design is carried out. It achieves good adaptability and can generate test paper quickly and successfully, which improves the efficiency of generating test paper. The simulation results show that, under the same constraints, the algorithm can generate test papers successfully, and improve the success rate and efficiency of generating test papers, and effectively optimize the problems in automatic generating test papers.

Intelligent test paper generation problem is a multi-objective parameter optimization problem under certain constraints, which has been realized by many algorithms. However, the existing intelligent test paper generation mostly adopts a single algorithm, and each algorithm has its own shortcomings. Sometimes it is inevitable to fall into the defects of the single algorithm in the process of test paper generation. Therefore, a mathematical model of intelligent test paper generation is proposed, which combines the advantages of artificial fish swarm algorithm and genetic algorithm to form a hybrid intelligent algorithm. At the beginning of intelligent test paper generation, the artificial fish swarm algorithm is used to quickly approach the test paper goal. In the process of test paper generation, when the optimal individual has no change or extremely small change in consecutive iterations, the genetic algorithm is used to jump the artificial fish individual to improve the convergence speed. The simulation results show that the hybrid intelligent algorithm can effectively optimize the effect of intelligent test paper generation by using a single algorithm alone.

In order to solve the problems such as blindfold search, slower convergence, and sometimes unsuccessfully search in the present genetic algorithms used for intelligent test paper generation, this paper introduces an improved genetic algorithm for intelligent test paper generation. This algorithm generates optimized initial chromosome group and controlling crossing and variation recurring to test paper parameters and randomization. Consequently, it can avoid the problems of blind search and slower convergence resulted by complete randomization controlling adopted by the present genetic algorithms. At last, this paper proves the improved genetic algorithm can better solve the intelligent test paper generation problem compared to present genetic algorithms mentioned in algorithm analysis section of this paper.

Conventional mathematics methods are extensively adopted in the most test paper generation systems, but these methods can't deal with multi-objective optimization question of more than one constrains effectively. This paper proposes an improved parallel GA(Genetic Algorithm) with good adaptability and universal applicability, which is used in test paper generation. The experiment results indicate that the algorithm is more adaptively and faster than conventional parallel GA, and improve the intelligent level of the test paper generation system effectively.

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.

Intelligent test paper generation belongs to an important research topic in the field of computer-aided education. Among them, Genetic Algorithm (GA), as an algorithm with high efficiency and performance, is widely used in the intelligent test paper generating system. But generally speaking, traditional GA always has some problems, such as local optimal solution and prematurity. The traditional genetic algorithm is improved in the aspects of the initial population, fitness function and some genetic operators to make it more in line with the requirements of intelligent paper generation, improving the efficiency and success ratio.

In this paper, we propose a genetic-simulated annealing algorithm (GSAA) with partial transmit sequence (PTS) technique (GSAA-PTS) to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) system. Genetic algorithm based PTS method (GA-PTS) is a novel and suboptimal PAPR reduction approach, which has lower computational load than original PTS method. However, researchers still focus on how to improve the PAPR performance. The proposed method GSAA-PTS takes the advantages of genetic algorithm and simulated annealing algorithm to search the best phase factors of PTS. In this method, we initialize the population firstly, and then selection, crossover and mutation are used to generate the new population, finally using the simulated annealing algorithm to update each chromosome in the population. The simulation results show that the better performance of PAPR have been achieved by adopting GSAA-PTS scheme.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

In this paper, we investigate virtual machine (VM) scheduling problem for the downlink joint transmission (JT) of coordinated multi-point (CoMP) systems in centralized radio access networks (C-RAN). The inter-dependent computing tasks of users are centralized and scheduled on heterogeneous VMs to be executed. In order to achieve efficient scheduling, a priority based VM scheduling model is designed. Then, a delay-constrained and resource-constrained optimization problem to minimize the maximum completion time among users is formulated. Three scheduling algorithms including genetic algorithm (GA), genetic simulated annealing algorithm (GSAA) and packing algorithm (PA) are developed to solve the problem. Simulation results show that GSAA outperforms GA and PA in guaranteeing joint synchronization of all cooperating entities and fairness of users.

According to the traditional UMDH network modeling with the least square method to recognize parameters ,it's easy to fall into local minimum ,and with the result that the prediction effect is not ideal. This paper puts forward to combine the simulated annealing algorithm and genetic algorithm, and introduces the combined algorithm to the UMDH network which is used to identify some of its description type coefficient. In this paper ,it describes the simulated annealing genetic algorithm ,and constructs the UMDH network model based on this algorithm, and the model is applied to the simulation of debris flow prediction research ,forecast average relative error reached 3. 54%. The results show that the algorithm not only ensuring the global optimization but also preventing premature convergence, improve the UMDH network model of global and local searching optimal ability further.

In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Twointegration systems are proposed; integrated SA–GA-type1 and integrated SA–GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value ofmachining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA–GA.

Last-train timetable coordination is extremely complex because a number of transfer directions involve in the subway network. In this paper, transfer redundant time (TRT) and transfer binary variables (TBV) that affect transfer results are considered in the Markowitz mean-variance model. By adjusting running time and dwelling time, the model creates a high-quality timetable that greatly improves the efficiency of transferring passengers. Furthermore, a genetic simulated annealing (GSA) algorithm is developed to solve this problem in the Beijing subway network, which consists of 14 lines, 17 transfer stations, and 42 key directions. The present model increases the number of successful connections by 40.0% and reduces the amount of just-missed connections by 83.3%, respectively. In addition, the mean-variance model significantly improves the subway network accessibility compared with the current practice of the last-train timetable.

Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites

In this paper, on the basis of the analysis of common market model and some economic theories in the cloud computing resource management process, we propose a cloud resource management model based on combinatorial double auction. In order to solve the winner determination problem (WDP) in the combinatorial double auction, a cloud resource combinatorial double auction algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Simulation results reveal that the algorithm combines genetic algorithm with simulated annealing algorithm (SAGA) outperforms genetic algorithm on fitness value and stability, and as the number of bidders increase, the solution have higher fitness value can be obtained.