摘要：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.
关键词：computational intelligence;heuristics;hybrid genetic algorithms;tabu search;combinatorial optimization;grey pattern quadratic assignment problem
