Optimizing a realistic largescale frequency assignment problem using a new parallel evolutionary approach
Abstract
This article analyses the use of a novel parallel evolutionary strategy to solve complex optimization problems. The work developed here has been focused on a relevant realworld problem from the telecommunication domain to verify the effectiveness of the approach. The problem, known as frequency assignment problem (FAP), basically consists of assigning a very small number of frequencies to a very large set of transceivers used in a cellular phone network. Real data FAP instances are very difficult to solve due to the NPhard nature of the problem, therefore using an efficient parallel approach which makes the most of different evolutionary strategies can be considered as a good way to obtain highquality solutions in short periods of time. Specifically, a parallel hyperheuristic based on several metaheuristics has been developed. After a complete experimental evaluation, results prove that the proposed approach obtains very highquality solutions for the FAP and beats any other result published.
 Publication:

Engineering Optimization
 Pub Date:
 August 2011
 DOI:
 10.1080/0305215X.2010.521241
 Bibcode:
 2011EnOp...43..813C
 Keywords:

 parallel hyperheuristic;
 frequency assignment problem;
 realistic frequency planning;
 parallel heuristic based on metaheuristics