Dynamic path planning in uncertain or hostile environments is of considerable importance in the field of crisis and disaster operations as well as for military operations research. The applications comprise a wide a range of network topologies related to the management of relief networks after natural disasters, the navigation of rescue robots in dangerous zones and the operation of sensor or communication networks. In this paper, a multi-layered adaptive network approach for shortest path planning in dynamically changing and uncertain environments is presented. We propose a slime mold-based optimization algorithm (SLIMO) that integrates situational awareness through multiple information layers. The slime mold evolution, its growth and tube dynamics, is governed by dynamical changes of the multiple layers. The paper presents an analysis of the algorithms' robustness w.r.t. the parameter settings. Sophisticated data farming experiments based on design of experiments approaches are conducted to explore the parameter space with regard to various growth strategies. As a result, robust parameter constellations for various applications can be determined. In this way, the adaptive network approach can be used for various applications in the field of dynamic path planning in critical operations.