Papers by Keyword: Niche Genetic Algorithm

Abstract: In view of fuel consumption problem of the formation establishment, starting from the fly around equation, using double-impulse, the optimization is simulated based on the Niche Genetic Algorithm (NGA). The simulation result shows that the NGA is a good way to solve the fuel consumption optimization problem of formation establishment, and the fuel consumption when the phase determined is greater than the fuel consumption when the phase not determined.

Abstract: In view of the multi-satellite formation establishment, the fuel consumption was optimized based on the Niche Genetic Algorithm. Some conclusions were drawn.

Abstract: The Multidimensional 0-1 knapsack problem is a NP hard problem, though there are many algorithm is used to solve the problem, but there is still not a good solution to solving the problem. This paper improved niche genetic algorithm, established a master-slave mode niche genetic algorithm, and carried on adaptive setting the individual Euclidean distance criterion, making it can changed with the evolving algebra incremental. At last, used master-slave niche genetic algorithm to solve the Multidimensional 0-1 knapsack problem, test results showed, the algorithm has good applicability and superiority in solving the Multidimensional 0-1 knapsack problem.

Abstract: Current advanced Fuzzing technique can only implement vulnerability mining on a single vulnerable statement each time, and this paper proposes a new multi-dimension Fuzzing technique, which uses niche genetic algorithm to generate test cases and can concurrently approach double vulnerable targets with the minimum cost on the two vulnerable statements each time. For that purpose, a corresponding mathematical model and the minimum cost theorem are presented. The results of the experiment show that the efficiency of the new proposed Fuzzing technique is much better than current advanced Fuzzing techniques.

Abstract: To solve the fatigue damage location problem of helicopter moving component, a new approach for linear location of acoustic emission (AE) source based on least squares support vector machine for regression (LS-SVR) and niche genetic algorithm (NGA) was proposed. Several time domain parameters of AE signal were taken as the inputs, and the linear coordinates of the breakpoints as the output. The sharing function based niche genetic algorithm is used to select the LS-SVR parameters automatically. The results of pencil lead break location experiment on specimen of carbon fiber materials indicate that the proposed approach can implement linear location of AE source effectively, and has better performance on convergence rate and location accuracy than RBF and BP neural network.

Abstract: Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.