A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

Date

2013-07-01

Author

Tosun, Umut
Dokeroglu, Tansel
Coşar, Ahmet

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The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor's solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.

Subject Keywords

Genetic algorithms, Parallel algorithms, İsland parallel genetic algorithm, Quadratic assignment problem

URI

https://hdl.handle.net/11511/31259

Journal

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

DOI

https://doi.org/10.1080/00207543.2012.746798

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

U. Tosun, T. Dokeroglu, and A. Coşar, “A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem,” INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, pp. 4117–4133, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31259.