Neural-Based Routing Method for Alleviating Congestion in Complex Networks

Abstract

In this paper, we propose a new packet routing strategy that incorporates memory information for reducing congestion in communication networks. First, we study the conventional routing strategy which selects the paths for transmitting packets to destinations using the distance information and the dynamical information such as the number of accumulating packets at adjacent nodes. Then, we evaluate the effectiveness of this routing strategy for the scale-free networks. From results of numerical simulations, we conclude that this routing strategy is not effective when the density of the packets increases due to the impermeability of the communication network. To avoid this undesirable problem, we incorporate memory information to the routing strategy. By using memory information effectively, packets are spread into the communication networks, achieving a higher performance than conventional routing strategies for various network topologies, such as scale-free networks, small-world networks, and scale-free networks with community structures.

Solving the Steiner tree problem in graphs by chaotic search

“…On the other hand, as one of the effective metaheuristics, an algorithm using chaotic dynamics, or the chaotic search, has already been proposed to escape from undesirable local minima. The chaotic search shows good performance for solving various N P-hard combinatorial optimization problems, such as the traveling salesman problems [13][14][15][16], the quadratic assignment problems [17][18][19], the vehicle routing problems [20,21], the packet routing problems [22][23][24][25][26][27][28][29], and the motif extraction problems [30,31]. However, the Steiner tree problem in graphs has a different feature from the abovementioned combinatorial optimization problems; local searches we introduced in this paper produce not only feasible solutions but also infeasible solutions during the search.…”