Abstract
The multidimensional knapsack problem (MKP) is an NP-hard combinatorial optimization problem whose solution consists of determining a subset of items of the maximum total profit that does not violate capacity constraints. Due to its hardness, large-scale MKP instances are usually a target for metaheuristics, a context in which effective feasibility maintenance strategies are crucial. In 1998, Chu and Beasley proposed an effective heuristic repair that is still relevant for recent metaheuristics. However, due to its deterministic nature, the diversity of solutions such heuristic provides is not sufficient for long runs. As a result, the search ceases to find new solutions after a while. This paper evaluates the use of efficiency groups to define a randomization strategy for the heuristic repair that increases the variability of the repaired solutions, without deteriorating quality and improves the overall results. We compared our randomized heuristic repair against the original one in 270 or-library instances, with improvements at the running time and solution quality found for many of them.
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