An efficient local search heuristic with row weighting for the unicost set covering problem

•A new algorithm was developed for the unicost set covering problem.•It integrates ideas of adaptive row weighting, a tabu list, and timestamps.•It was evaluated on 91 benchmark problems with up to millions of variables.•It improved the best known solutions on 14 problems. The Set Covering Problem (SCP) is NP-hard. We propose a new Row Weighting Local Search (RWLS) algorithm for solving the unicost variant of the SCP, i.e., USCPs where the costs of all sets are identical. RWLS is a heuristic algorithm that has three major components united in its local search framework: (1) a weighting scheme, which updates the weights of uncovered elements to prevent convergence to local optima, (2) tabu strategies to avoid possible cycles during the search, and (3) a timestamp method to break ties when prioritizing sets. RWLS has been evaluated on a large number of problem instances from the OR-Library and compared with other approaches. It is able to find all the best known solutions (BKS) and improve 14 of them, although requiring a higher computational effort on several instances. RWLS is especially effective on the combinatorial OR-Library instances and can improve the best known solution to the hardest instance CYC11 considerably. RWLS is conceptually simple and has no instance-dependent parameters, which makes it a practical and easy-to-use USCP solver.