A fast and efficient solution to the Capacity Assignment problem using discretized learning automata

The Capacity Assignment (CA) problem focuses on finding the best possible set of capacities for the links that satisfies the traffic requirements in a prioritized network while minimizing the cost. Most approaches consider a single class of packets flowing through the network, but in reality, different classes of packets with different packet lengths and priorities are transmitted over the networks. In this paper we assume that the traffic consists of different classes of packets with different average packet lengths and priorities. Marayuma and Tang [7] proposed a single algorithm composed of several elementary heuristic procedures. Levi and Ersoy [6] introduced a simulated annealing approach which produced substantially better results. A new method that uses continuous learning automata was introduced in a previous paper [12]. In this paper we introduce a new method which uses discretized learning automata to solve the problem. Indeed, to the best of our knowledge, this is the fastest and most accurate scheme currently available.