The Efect of Queueing Strategy on Network Trafc

In recent years,the transportation system has been faced by increasing challenge in congestion and inefciency,and research in trafc network has become a significant area of interest.In this paper,we introduce a dynamic-information-based(DIB)queueing strategy into network trafc model under the efcient routing strategy.DIB makes a packet with higher priority to be delivered if there are less packets travelling along its path from the current node to the destination.It is found that,compared with the traditional first-in-first-out(FIFO)queueing strategy,DIB can efectively balance the trafc load of the system via delaying packets to be delivered to congested nodes.Although the network capacity has no obvious changes,some other indexes which reflect transportation efciency are efciently improved in the congestion state.Besides,extensive simulation results and discussions are provided to explain the phenomena.The results may provide novel insights for research on trafc systems.     

An improved genetic algorithm with dynamic topology

The genetic algorithm(GA) is a nature-inspired evolutionary algorithm to find optima in search space via the interaction of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topologies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence.