An improved routing algorithm using chaotic neurodynamics for packet routing problems

Abstract

Abstract:Combinatorial optimization problems consist of static problems such as the traveling salesman problem and the quadratic assignment problem, and dynamic problems such as the packet routing problem and the traffic flow control problem. In static combinatorial optimization problems, the search space for the solution does not change over time and, therefore, neither does the optimal solution. On the contrary, in dynamic combinatorial optimization problems, the search space always changes. Thus, there is no guarantee that the optimal solution for one iteration also applies to the next iteration. In the context of dynamic combinatorial optimization problems, we propose in this paper a heuristic routing method that uses chaotic neurodynamics and degree information to solve the packet routing problem. Numerical experiments showed that the proposed method improves average arrival rate by approximately 130% over the conventional shortest hop method.

A routing method with adaptively adjusting memory information based on local routing history

“…This study used communication network models represented by an unweighted and undirected graph U = ( V , E ), where V is a set of nodes, and E is a set of edges [ 2 , 4 , 9 – 11 , 20 , 24 , 28 , 29 ]. In the communication network model, each node represents a host and a router, and each edge represents a connection between the nodes.…”

“…evaluation function for this routing method as follows: 8), (10), (18), (19) and (20) compose the memory method with fixed transmitting probabilities (hereafter, we call this method memory-pfix. The memory-pfix method transmits the packets using the static paths determined by the distance and modified memory information.…”

“…From this viewpoint, a routing method that autonomously diversifies packet transmissions using chaotic neurodynamics has been proposed [ 19 , 20 ]. An advantage of using chaotic neurodynamics is the refractory effect, which inhibits the firing of neurons for a certain period of time [ 21 ].…”

Strategy for Exploring Feasible and Infeasible Solution Spaces to Solve a Multiple-Vehicle Bike Sharing System Routing Problem

“…However, the local search methods cannot find the optimal solution when they are trapped in local minima. To avoid local minima, or to jump out of a local minimum in the search space, several metaheuristics are proposed, such as genetic algorithms [19], simulated annealing [20], neural networks [21][22][23] and the tabu search [24,25]. In this study, we used the tabu search to find better solutions for mBSSRP [24,25].…”

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.…”