Metaheuristics are dilemma-independent methods that are generalizedin a variety of problems. In the real world, various problems are solved using generalized dilemma-independent methods called Metaheuristics Computation. Metaheuristic Nature Inspired Computing (MNIC) is a generalized approach to solve NP-hard problems by taking inspirations from the behavior of mother biological nature and their characteristics. Mining of Association rule, Frequent Itemset and High Utility Itemset are strongly interrelated and developing in the field of Data Mining. Metaheuristic nature inspired computation was widely used for the mining association rules of frequentitemsets and high utility itemsets to address the high computation time and optimal solutions. While various articles have been written, there is no systematic review of contemporary metaheuristic nature inspired approaches used in Association Rule Mining (ARM), Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM). This paper explores recent literature on various metaheuristics nature inspired approaches used for ARM, FIM and HUIM.

Metaheuristics 是一种与困境无关的方法，可以推广到各种问题中。在现实世界中，使用称为元启发式计算的广义困境独立方法来解决各种问题。Metaheuristic Nature Inspired Computing (MNIC) 是一种广义的方法，通过从生物母性的行为及其特征中获取灵感来解决 NP-hard 问题。关联规则、频繁项集和高效用项集的挖掘在数据挖掘领域有着密切的联系和发展。元启发式自然启发计算被广泛用于频繁项集和高效用项集的挖掘关联规则，以解决高计算时间和最优解的问题。虽然写了各种各样的文章，没有对关联规则挖掘 (ARM)、频繁项集挖掘 (FIM) 和高效用项集挖掘 (HUIM) 中使用的当代元启发式自然启发方法进行系统评价。本文探讨了有关用于 ARM、FIM 和 HUIM 的各种元启发式自然启发方法的最新文献。