On solving the capacity assignment problem using continuous learning automata
The Capacity Assignment problem focuses on finding the best possible set of capacities for the links that satisfy the traffic requirements in a prioritized network while minimizing the cost. Apart from the traditional methods for solving this NP-Hard problem, one new method that uses Learning Automata (LA) strategies has been recently reported. This method uses discretized learning automata . The present paper shows how the problem can be solved using continuous learning automata. The paper considers the realistic scenario when different classes of packets with different packet lengths and priorities are transmitted over the networks. After presenting the two wellknown solutions to the problem (due to Marayuma and Tang , and Levi and Ersoy ) we introduce our new method that uses continuous LA, which is comparable to the discretized version, and is probably the fastest and most accurate scheme available.