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An Improved Intelligent Ant Colony Algorithm for the Reliability Optimization Problem in Cyber-Physical Systems

机译:一种改进的智能蚁群算法,解决网络物理系统的可靠性优化问题

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摘要

In this paper the torsion bar optimization problem of reliability is considered. Since this problem is a difficult optimization problem, an improved intelligent ant colony algorithm is proposed to solve the problem. This algorithm comprises five stages. First stage is the initialization of pheromone and the sensor node configuration. The second stage is to select the next sensor node. The third stage is to update the pheromone of the sensor node path. The fourth stage is to acquire the best path by computing the shortest distance. The last stage is to output the global optimal solution. To evaluate performance of the proposed algorithm, it is compared with the ant colony optimization algorithm and the genetic algorithm. The experimental results show that the proposed algorithm performs better than them.
机译:本文考虑了扭杆的可靠性优化问题。由于该问题是困难的优化问题,因此提出了一种改进的智能蚁群算法来解决该问题。该算法包括五个阶段。第一步是信息素的初始化和传感器节点的配置。第二阶段是选择下一个传感器节点。第三阶段是更新传感器节点路径的信息素。第四阶段是通过计算最短距离来获得最佳路径。最后一步是输出全局最优解。为了评估该算法的性能,将其与蚁群优化算法和遗传算法进行了比较。实验结果表明,所提算法的性能优于它们。

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