In search and rescue operations, an efficient search path, colloquially understood as a path maximizing the probability of finding survivors, is more than a path planning problem. Maximizing the objective adequately, i.e., quickly enough and with sufficient realism, can have substantial positive impact in terms of human lives saved. In this paper, we address the problem of efficiently optimizing search paths in the context of the NP-hard optimal search path problem with visibility, based on search theory. To that end, we evaluate and develop ant colony optimization algorithm variants where the goal is to maximize the probability of finding a moving search object with Markovian motion, given a finite time horizon and finite resources (scans) to allocate to visible regions. Our empirical results, based on evaluating 96 variants of the metaheuristic with standard components tailored to the problem and using realistic size search environments, provide valuable insights regarding the best algorithm configurations. Furthermore, our best variants compare favorably, especially on the larger and more realistic instances, with a standard greedy heuristic and a state-of-the-art mixed-integer linear program solver. With this research, we add to the empirical body of evidence on an ant colony optimization algorithms configuration and applications, and pave the way to the implementation of search path optimization in operational decision support systems for search and rescue.

在搜索和救援行动中，有效的搜索路径，通俗地理解为最大化找到幸存者概率的路径，不仅仅是路径规划问题。充分地最大化目标，即足够快并具有足够的现实性，可以对挽救人类生命产生重大的积极影响。在本文中，我们基于搜索理论解决了在具有可见性的 NP-hard 最优搜索路径问题的背景下有效优化搜索路径的问题。为此，我们评估和开发蚁群优化算法变体，其目标是在给定有限时间范围和分配给可见区域的有限资源（扫描）的情况下，最大化找到具有马尔可夫运动的移动搜索对象的概率。我们的实证结果，基于使用针对问题定制的标准组件评估元启发式的 96 种变体并使用实际大小的搜索环境，提供有关最佳算法配置的宝贵见解。此外，我们的最佳变体与标准贪婪启发式算法和最先进的混合整数线性程序求解器相比，尤其是在更大和更现实的实例上更胜一筹。通过这项研究，我们增加了蚁群优化算法配置和应用的实证证据，并为在搜索和救援的运营决策支持系统中实施搜索路径优化铺平了道路。我们的最佳变体与标准贪婪启发式算法和最先进的混合整数线性规划求解器相比，尤其是在更大和更现实的实例上具有优势。通过这项研究，我们增加了蚁群优化算法配置和应用的实证证据，并为在搜索和救援的运营决策支持系统中实施搜索路径优化铺平了道路。我们的最佳变体与标准贪婪启发式算法和最先进的混合整数线性规划求解器相比，尤其是在更大和更现实的实例上具有优势。通过这项研究，我们增加了蚁群优化算法配置和应用的实证证据，并为在搜索和救援的运营决策支持系统中实施搜索路径优化铺平了道路。