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Article dans une revue

Memetic search for the quadratic assignment problem

Abstract :

The quadratic assignment problem (QAP) is one of the most studied NP-hard problems with various practical applications. In this work, we propose a powerful population-based memetic algorithm (called BMA) for QAP. BMA integrates an effective local optimization algorithm called Breakout Local Search (BLS) within the evolutionary computing framework which itself is based on a uniform crossover, a fitness-based pool updating strategy and an adaptive mutation procedure. Extensive computational studies on the set of 135 well-known benchmark instances from the QAPLIB revealed that the proposed algorithm is able to attain the best-known results for 133 instances and thus competes very favorably with the current most effective QAP approaches. A study of the search landscape and crossover operators is also proposed to shed light on the behavior of the algorithm.

Type de document :
Article dans une revue
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Soumis le : lundi 1 juin 2020 - 16:53:29
Dernière modification le : mercredi 20 octobre 2021 - 03:19:08




Una Benlic, Jin-Kao Hao. Memetic search for the quadratic assignment problem. Expert Systems with Applications, Elsevier, 2015, 42 (1), pp.584-595. ⟨10.1016/j.eswa.2014.08.011⟩. ⟨hal-02709497⟩



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