Optimizing a realistic large-scale 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 real-world 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 NP-hard 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 high-quality solutions in short periods of time. Specifically, a parallel hyper-heuristic based on several meta-heuristics has been developed. After a complete experimental evaluation, results prove that the proposed approach obtains very high-quality 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 hyper-heuristic;
- frequency assignment problem;
- realistic frequency planning;
- parallel heuristic based on metaheuristics