Continuous learning automata solutions to the capacity assignment problem

The Capacity Assignment (CA) problem focuses on finding the best possible s et 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, diffe rent classes of packets with different packet lengths and priorities are tr ansmitted over the networks. In this paper, we assume that the traffic cons ists of different classes of packets with different average packet lengths and priorities. We shall look at three different solutions to this problem. Marayuma and Tang [9] proposed a single algorithm composed of several elem entary heuristic procedures. Levi and Ersoy [8] introduced a simulated anne aling approach that produced substantially better results. In this paper, w e introduce a new method which uses continuous learning automata to solve t he problem. Our new schemes produce superior results when compared with eit her of the previous solutions and is, to our knowledge, currently the best known solution.