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Estimation of distribution algorithm with 2-opt local search for the quadratic assignment problem (2006)
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| Venue: | Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithm |
| Citations: | 6 - 1 self |
BibTeX
@INPROCEEDINGS{Zhang06estimationof,
author = {Qingfu Zhang and Jianyong Sun and Edward Tsang and John Ford},
title = {Estimation of distribution algorithm with 2-opt local search for the quadratic assignment problem},
booktitle = {Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithm},
year = {2006},
pages = {281--292},
publisher = {Springer-Verlag}
}
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Abstract
Abstract. This chapter proposes a combination of estimation of distribution algorithm (EDA) and the 2-opt local search algorithm (EDA/LS) for the quadratic assignment problem (QAP). In EDA/LS, a new operator, called guided mutation, is employed for generating new solutions. This operator uses both global statistical information collected from the previous search and the location information of solutions found so far. The 2-opt local search algorithm is applied to each new solution generated by guided mutation. A restart strategy based on statistical information is used when the search is trapped in a local area. Experimental results on a set of QAP test instances show that EDA/LS is comparable with the memetic algorithm of Merz and Freisleben and outperforms estimation of distribution algorithm with guided local search (EDA/GLS). The proximate optimality principle on the QAP is verified experimentally to justify the rationale behind heuristics (including EDA/GLS) for the QAP. 1
Keyphrases
distribution algorithm quadratic assignment problem eda l 2-opt local search new solution 2-opt local search algorithm eda gls restart strategy guided local search local area qap test instance previous search global statistical information proximate optimality principle statistical information new operator location information experimental result memetic algorithm guided mutation
