A restart local search algorithm for solving maximum set k-covering problem
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The maximum set k-covering problem (MKCP) is a famous combinatorial optimization problem with widely many practical applications. In our work, we design a restart local search algorithm for solving MKCP, which is called RNKC. This algorithm effectively makes use of several advanced ideas deriving from the random restart mechanism and the neighborhood search method. RNKC designs a new random restart method to deal with the serious cycling problem of local search algorithms. Thanks to the novel neighborhood search method that allows a neighborhood exploration of as many feasible search areas as possible, the RNKC can obtain some greatly solution qualities. Comprehensive results on the classical instances show that the RNKC algorithm competes very favorably with a famous commercial solver CPLEX. In particular, it discovers some improved and great results and matches the same solution quality for some instances.
KeywordsRandom restart Neighborhood search Maximum set k-covering problem
We would like to thank the anonymous referees for their helpful comments. This work was supported in part by NSFC under Grant Nos. (61402196, 61272208, 61672261, 61133011, 61170092, 61003101) and the China Postdoctoral Science Foundation under Grant No. 2013M541302.
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Conflict of interest
The authors declare that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
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