| Abstract: | The set-union knapsack problem(SUKP) is proved to be a strongly NP-hard problem, and it is an extension of the classic NP-hard problem: the 0-1 knapsack problem(KP). Solving the SUKP through exact approaches is computationally expensive. Therefore, several swarm intelligent algorithms have been proposed in order to solve the SUKP. Hyper-heuristics have received notable attention by researchers in recent years, and they are successfully applied to solve the combinatorial optimization problems. In this article, we propose a binary particle swarm optimization(BPSO) based hyper-heuristic for solving the SUKP, in which the BPSO is employed as a search methodology. The proposed approach has been evaluated on three sets of SUKP instances. The results are compared with 6 approaches: BABC, EMS, gPSO, DHJaya, b WSA, and HBPSO/TS, and demonstrate that the proposed approach for the SUKP outperforms other approaches. |
