We tackle the Thief Orienteering Problem (ThOP), which is academic multi-component problem: it combines two classical combinatorial problems, namely the Knapsack Problem (KP) and the Orienteering Problem (OP). In this problem, a thief has a time limit to steal items that distributed in a given set of cities. While traveling, the thief collects items by storing them in their knapsack, which in turn reduces the travel speed. The thief has as the objective to maximize the total profit of the stolen items. In this article, we present an approach that combines swarm-intelligence with a randomized packing heuristic. Our solution approach outperforms existing works on almost all the 432 benchmarking instances, with significant improvements.

翻译：我们处理盗贼东方问题(THOP),这是一个学术性的多组成部分问题:它结合了两个古典的组合问题,即Knappsack问题(KP)和东方问题(OP)。在这个问题上,盗贼有时间窃取在特定城市中分发的物品。在旅行时,盗贼通过将物品储存在他们的背包中收集物品,这反过来又降低了旅行速度。盗贼的目标是最大限度地增加被盗物品的全部利润。在这个文章中,我们提出了一个方法,把群集智能与随机包装超常结合起来。我们的解决方案方法比几乎所有的432基准案例都好,并有显著改进。