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Decomposition Tehniques for Solving Frequency Assigment Problems (FAP) - A Top-Down Approach

This paper deals with the resolution of MI-FAP (Minimum Interference Frequency Assignment Problems). 
 
Because of the NP-hardness of this problem, it is still difficult to cope with real FAP instances with exact 
 
and even with heuristic algorithms. To address this problem, this paper aims at solving MI-FAP by using a 
 
decomposition and it proposes a generic Top-Down approach for the resolution. The main idea behind the 
 
generic aspect of our approach is to think at the same time the decomposition and the resolution steps. First, 
 
it allows us to explore different decomposition methods and different resolution methods as well. Second, 
 
it makes free to guide the decomposition method for a specific solving method. Mainly we present two 
 
generic combinations of decomposition and resolution called Top-Down and Iterative Top-Down algorithms. 
 
To validate these propositions, we choose two decomposition algorithms based on the Min-Cut decomposition 
 
and one efficient genetic algorithm for MI-FAP resolution. The first experimental results demonstrate a good 
 
trade-off between the quality of the solution and the execution time of this approach. It encourages us to follow 
 
the study in this direction.