earticle

영어

The combination of traditional ant colony algorithm in solving the optimization process to consume a large amount of time, easily falling into local optimal solution and convergence is slow and other disadvantages, while also generating a lot of useless redundant iterative code, operation efficiency is low. Therefore, ant colony optimization algorithm is proposed. The algorithm based on genetic algorithm has the ability to search the global ant colony algorithm also has a parallel and positive feedback mechanisms. Changes in the use of genetic algorithm selection operator, crossover operator and mutation operator action to determine the distribution of pheromone on the path, the ant colony algorithm for feature selection using support vector machine classifiers for evaluating the performance characteristics of the feedback sub-Variorum And by changing the pheromone iteration, parameter selection and increase the local pheromone update feature nodes guided the re-combination. The algorithm uses probability expectation values are obtained to meet under the conditions with minimal sensor nodes, and gives the optimal coverage and connectivity probability models and reasoning. The experimental results show that, the algorithm can not only use the least nodes complete the effective target area to be covered, and in reducing the network energy consumption is also greatly improved, simultaneously reduces the cyber source configuration, improve the network life cycle.