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As an effective global search method, genetic algorithm has obvious advantages. But it usually has problems of premature convergence and local optimum in practical application. According to this, a new algorithm with improved selection, crossover and mutation is proposed. Through the simulation experiments, the improved algorithm shows its faster convergence and better stability. It is valid which...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Under the analysis of the present situation of urban roads at home and abroad, the paper reviews the development of tripper guiding information system, traffic-flow distribution model and route choice model. Then it has three multi-attribute decision-making model based on the influence factors of tripper route choice and uncertainty theories, in order to analyze the decision-making process of tripper...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
Environmental impact assessment has an important influence on project decision. Based on the fuzzy matter element theory and information entropy theory, entropy fuzzy matter-element model has been established, which is used to evaluate the good or bad of schemes. Taking scheme selection of highway as an example, the evaluation results show that the model can effectively eliminate the impact of man-made...
Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multimodal functions. To improve PSO performance on global optimization problems, this paper...
Proximity ranking according to end-to-end network distances (e.g., Round-Trip Time, RTT) can reveal detailed proximity information, which is important in network management and performance diagnosis in distributed systems. However, to the best of our knowledge, there has been no similar work on this subject in the P2P computing field. We present a distributed rating method iRank, that enables proximity...
This paper proposes fusion of synchronous germ computing (SGC) with twin swarm intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions...
The feature subset selection is a key preprocessing part in the detection of the stored-grain insects based on the image recognition technology. According to the global optimization ability of the particle swarm optimization (PSO) and the superior classification performance of the support vector machines (SVM), this study proposed a method based on PSO and SVM to improve the classification accuracy...
In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums,...
An optimized method to design and implement digital three-phase phase-locked loop (PLL) based on FPGA is presented in this paper. The PLL fits in electric power system as well as other fields. At first, principle and basic structure of the PLL including phase discriminator, loop filter and voltage controlled oscillator (VCO) are introduced, then these modules are designed in VHDL language with blocking...
Some methods that combine FEM (finite element method) and nonlinear programming are applied to optimization design of high voltage electric field. The use of FEM as a cost function evaluator is constrained by the sequential processes of geometry construction, meshing, and solving in the post-processing stages. The computational overhead makes FEM-based optimization unattractive despite its accuracy...
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