This paper presents the Fennec Adaptive Cooling Algorithm (FACA), a novel metaheuristic inspired by the thermoregulation strategies of the Fennec fox, a species native to the Algerian desert. This algorithm leverages the adaptive cooling mechanisms of the Fennec fox to balance exploration and exploitation in the optimization process. We detail the development and implementation of FACA, showcasing its application to a warehouse location problem. The algorithm's effectiveness is demonstrated through comparative analysis with other established metaheuristics, highlighting FACA's potential in achieving optimal solutions while satisfying logistical constraints. This research not only introduces a new optimization tool but also underscores the importance of local biodiversity as a source of inspiration for solving practical problems.