Continuous learning automata solutions to the capacity assignment problem

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. We shall look at three different solutions to this problem. K. Marayuma and D.T. Tang (1977) proposed a single algorithm composed of several elementary heuristic procedures. A. Levi and C. Ersoy (1994) introduced a simulated annealing approach that produced substantially better results. In this paper, we introduce a new method which uses continuous learning automata to solve the problem. Our new schemes produce superior results when compared with either of the previous solutions and is, to our knowledge, currently the best known solution