The multidimensional knapsack problem (MKP) is an NP-hard combinatorial optimization problem whose solution is determining a subset of maximum total profit items that do 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 insufficient for long runs. As a result, the search for new solutions ceases after a while. This paper proposes an efficiency-based randomization strategy for the heuristic repair that increases the variability of the repaired solutions without deteriorating quality and improves the overall results.

翻译：多维背包问题（MKP）是一个NP难组合优化问题，其解是确定一个总利润最大且不违反容量约束的物品子集。由于该问题的复杂性，大规模MKP实例通常成为元启发式算法的求解目标，在此背景下有效的可行性维护策略至关重要。1998年，Chu和Beasley提出了一种高效的启发式修复方法，该方法至今仍适用于现代元启发式算法。然而，由于其确定性本质，该启发式方法提供的解多样性在长期运行中不足，导致对新解的搜索在一定时间后停滞。本文提出一种基于效率的启发式修复随机化策略，该策略能在不降低解质量的前提下增加修复解的多样性，从而提升整体求解效果。