# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### On-Line Evacuation Wayfinding Algorithms
#### 3.2.5 Biologically-Inspired Algorithms  
Millions of years of evolution have made animal's foraging behaviours become near-optimal solutions of autonomous search and path-finding [135]. Biologically-inspired approaches, which are inspired by simple, but reliable natural mechanisms, employ heuristics to search optimal routes in a computationally-efficient manner. For instance, a feed-forward neural network model was adopted for a wireless sensor-actuator network (WSAN) for evacuation routing in [95]; all physical nodes in the WSAN deployed a neural network with an identical topology: an input layer, a hidden layer and an output layer; the input layer received the latest two coordinates of a pedestrian, and a suggested direction was subsequently generated by the output layer; the neural networks were trained with a back-propagation algorithm [136] in standard situations and were deactivated when an emergency happened; hence evacuees would be directed to exits over their normal walking paths; however, back-propagation algorithms suffer from a slow learning rate and easily converge to local minima; furthermore, this model cannot react to the spreading of a hazard. The study in [96] employed a genetic algorithm [137; 138] to minimise the total evacuation time, travel distance and number of congestions encountered during an evacuation process. Non-domination sorting [139] was used as no a priori knowledge was available to determine the weight of the three goals; the initial \"chromosomes\" were paths found by the \(k\)-th shortest path algorithm [140] and were incrementally evolved to feasible solutions through crossover and mutation with respect to the path length, congestion level and hazard intensity. As an evolutionary approach, this algorithm had advantages in solving the multi-objective optimization problem (MOP) [141]; however, the computational overhead was relatively high due to the path-finding and the evolution process.  
The research in [142] adopted a variation of particle swarm optimization (PSO) to search for routes and adjust velocity during evacuations; occupants were viewed as particles to search exits; once an exit was discovered, all the other particles would move towards it while keeping their moving inertia to expand the searching space; if more than one exit was found, particles would choose the nearest exit as the destination; nevertheless, using occupants directly to explore paths may cause severe injuries and fatalities; meanwhile, this algorithm may induce serious congestion and oscillation problems. Inspired by the bee colony foraging behaviour, the work in [143] used bee colony optimization [144] to displace evacuees in hazardous areas to safe areas during an emergency evacuation; hives, food sources and bees represented safe areas, hazardous areas and evacuees, respectively; evacuees selected a safe area with regard to \"attractiveness\", which was determined by the distance to the area and the distribution of people in hazardous areas; once an evacuee determined a target, it would recruit other evacuees by sharing information of the devoted area; this algorithm obtained a robust evacuation plan at the expense of relatively high communication overhead.