The critical issue identified in recent times is the high utility itemset mining (HUIM). It may be used for showing products that are profitably utilizing considering the factors of profit and quality as opposed to the frequent itemset (FIM) or the association rule (ARM) mining. There are numerous high utility itemset mining (HUIs) algorithms, mostly designed for handling the exponential search space to discover the HUIs at the time the number of the distinct items and the database that was very large. For this purpose, a meta-heuristic algorithm is designed for HUIs mining, working based on the genetic algorithm (GA) and the Dolphin echolocation optimization (DEO). The intended purpose of this evolutionary computation (EC) techniques on the DEO, here only fewer parameters are required to be compared to the approaches that are based on the GA. As the traditional DEO mechanism had been found for handling this continuous problem, an efficient algorithm based on the DEO called the high-utility itemset mining-DEO (HUIM-DEO) was proposed. To prove that the algorithm proposed was able to outperform the other heuristic algorithms to mine the HUIs for the time taken for execution, the number of HUIs discovered, and their convergence.

最近发现的关键问题是高实用项集挖掘（HUIM）。它可以用于显示通过考虑利润和质量因素（与频繁项集（FIM）或关联规则（ARM）挖掘相反）来获利的产品。有很多高实用项集挖掘（HUI）算法，主要用于处理指数搜索空间，以在不同项和数据库数量很大时发现HUI。为此，基于遗传算法（GA）和海豚回声定位优化（DEO）设计了一种用于HUI挖掘的元启发式算法。这种基于DEO的进化计算（EC）技术的预期目的，此处与基于GA的方法相比，只需要较少的参数即可。由于已经找到了解决这种连续问题的传统DEO机制，因此提出了一种基于DEO的高效算法，即高效工具集挖掘DEO（HUIM-DEO）。为了证明所提出的算法能够在执行时间，发现的HUI数量及其收敛方面优于其他启发式算法来挖掘HUI。