A NEW HYBRID GENETIC ALGORITHM FOR THE GREY PATTERN QUADRATIC ASSIGNMENT PROBLEM

Alfonsas Misevičius, Evelina Stanevičienė

Abstract


In this paper, we propose an improved hybrid genetic algorithm for the solution of the grey pattern quadratic assignment problem (GP-QAP). The novelty is the hybridization of the genetic algorithm with the so-called hierarchical iterated tabu search algorithm. Very fast exploration of the neighbouring solutions within the tabu search algorithm is used. In addition, a smart combination of the tabu search and adaptive perturbations is adopted, which enables a good balance between diversification and intensification during the iterative optimization process. The results from the experiments with the GP-QAP instances show that our algorithm is superior to other heuristic algorithms. Many best known solutions have been discovered for the large-scaled GP-QAP instances.

DOI: http://dx.doi.org/10.5755/j01.itc.47.3.20728


Keywords


computational intelligence; heuristics; hybrid genetic algorithms; tabu search; combinatorial optimization; grey pattern quadratic assignment problem

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