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A hybrid artificial fish-school optimization algorithm for solving the quadratic assignment problem

The quadratic assignment problem (QAP) is a classic combinatorial optimization problem, which is of the NP-hard nature. In this paper, a hybrid artificial fish school optimization algorithm (HAFSOA) is proposed. In HAFSOA, the heuristic information is used in constructing some better initial individuals and its search ability of the global optimal solution is improved by a combination of the modified fish school optimization and differential evolution. In addition, by taking different visual distances for three behaviors: preying, clustering and following, the convergence speed of the proposed HAFSOA is speeded up. Many QAP experimental results show that the proposed HAFSOA can solve QAP better.