An Artificial Immune Approach for Optimizing Crowd Emergency Evacuation Route Planning Problem

Disastrous situations, either naturally (such as fires, earthquake, rising tides, hurricane) or man-made (such as terrorist bombings, chemical spills, and so on), have claimed the lives of thousands. As such, optimizing the evacuation operations during an emergency situation would require an effective crowd evacuation plan, which is acknowledged to be one of the vital studies of the societal research as well as emergency route planning (ERP) community. Several descriptions of prior developed approaches for emergency evacuation that encompassed the needs of a variety of public community as well as fulfilling the complexity of the situation, are summed up and discussed. This paper introduces an immune algorithm (IA) to optimize the evacuation plan for solving the ERP problems. The approach is first validated against previous work while further experimentation reveals the effectiveness of the proposed IA, with regard to certain parameter calibrations, in the context of ERP problems. The findings have been summarized and presented, whereas the potential for future work is identified.