The obnoxious p-median problem (OpM) is one of the NP-hard combinatorial optimization problems, in which the goal is to find optimal places to facilities that are undesirable (e.g. noisy, dangerous, or pollutant) such that the sum of the minimum distances between each non-facility location and its nearest facility is maximized. In this paper, for the first time in the literature, Iterated Greedy (IG) metaheuristic has been applied at a higher level to solve this problem. A powerful composite local search method has also been developed by combining two fast and effective local search algorithms, namely RLS1 and RLS2, which were previously used to solve the OpM. Comprehensive experiments have been conducted to test the performance of the proposed algorithm using a common benchmark for the problem. The computational results show the effectiveness of the IG algorithm that it can find high-quality solutions in a short time. Based on the set of selected instances, the results also reveal that the developed IG algorithm outperforms most of the state-of-the-art algorithms and contributes to the literature with 5 new best-known solutions.

令人讨厌的p中值问题（OpM）是NP-hard组合优化问题之一，其目的是找到不希望的设施（例如，嘈杂，危险或污染物），以使每个非工厂位置与其最近的工厂之间的最小距离之和最大。本文在文献中首次将迭代贪婪（IG）元启发式方法应用于更高级别，以解决此问题。通过组合两个以前用于解决OpM的快速有效的本地搜索算法，即RLS1和RLS2，还开发了一种功能强大的复合本地搜索方法。已经进行了全面的实验，以使用该问题的通用基准测试所提出算法的性能。计算结果表明了IG算法的有效性，它可以在短时间内找到高质量的解。根据所选实例的集合，