A guided evolutionary simulated annealing approach to the quadratic assignment problem

The quadratic assignment problem, one of the classical NP-complete problems, is usually interpreted as a facility layout problem, of which the task is to assign facilities to locations in a manner so as to minimize a total cost function. The complexity of the problem has motivated the development of many approximation procedures. In this paper, the authors present a new approach to the quadratic assignment problem (QAP). The new technique, called guided evolutionary simulated annealing (GESA), is a parallel algorithm. It combines the ideas of simulated evolution and simulated annealing in a novel way. The authors demonstrate the technique using facility layout problems as examples. Simulation results for benchmark problems are reported. The results indicate that the GESA method outperforms other approximation methods.<
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