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A quick convergent artificial bee colony algorithm for solving quadratic assignment problems
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DOI：10.1016/j.cie.2019.106070.png)
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Abstract
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Quadratic assignment problem (QAP) is an NP-hard problem. Because of this computational complexity, various search processes have been developed and used in different heuristics over the years to solve the QAPs. However, in most of those heuristics, the utility of adopting the particular search processes has not been justified. Apart from this, it has been a well-established fact that the convergence rate of any algorithm reduces significantly with the increase in size and flow density of the considered problem. This paper first describes how a quick convergent artificial bee colony (qABC) algorithm is developed using various search processes, and its performance is then evaluated and compared with some of the other state-of-the-art algorithms with respect to convergence, reliability and statistical significance of the results based on the selected problems available in the QAP-library. The comparison results conclude that the qABC algorithm can converge quickly, reliably and statistically more significantly as compared to other algorithms in most of the problem instances.
Keywords:
Combinatorial optimization
QAP
ABC
qABC
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