## 2 A Survey of Bio-Inspired Algorithms for Transportation Problems
### Algorithms for Location and Routing
[43] considered the routing of unmanned reconnaissance aerial vehicles on sparse graphs that mimic inaccessible zones, such as those hit by forest fires, earthquakes or other disasters, and developed a hybrid ACO algorithm, which incorporates local search heuristics for uncovering feasible routes within tractable times. Empirical results demonstrate the excellent convergence property and robustness of the algorithm in uncovering low risk and Hamiltonian visitation paths. [PERSON] and [PERSON] [44] also used ACO to solve two path selection problems in emergency logistics management. The first is a single-objective model to minimize total travel time along a path. The second is a multi-objective model to minimize the total travel time, as well as the path complexity, and it further considers the chaos, panic and congestion in times of disaster. However, the multi-objective model is converted into a single-objective model using weighted aggregation. [PERSON] _et al_. [45] developed an algorithm combing AIS and ACO, namely the immune ant colony optimization (IACO), for a transportation route choice problem in emergency logistics. The main idea is to obtain pheromone distributions from AIS and optimize the solutions by basic ACO. Computer simulation indicates that IACO has more effective timeliness to the model than basic ACO. [PERSON] _et al_. [46] proposed a novel bio-inspired algorithm, which is based on the mathematical model of amoeboid organism, to solve the optimal route selection problem in disaster extension. The problem considers both the travel time and the path length. The algorithm uses the \(k\)-th shortest path program to find out the first to \(k\)-th shortest path, uses the amoeboid algorithm to find the longest path to construct the dimensionless indexes and, finally, obtains the optimal routes. Considering vehicle route choosing in urban emergency management, [PERSON] and [PERSON] [47] proposed an interesting approach that employs ACO for searching optimal solutions to the problem, meanwhile using PSO to optimize the main parameters of the ACO. Simulation results show that the approach has a good convergence performance, meanwhile effectively avoiding being trapped by local optima. Location and routing are often interrelated. [PERSON] _et al_. [48] studied transit bus operations for emergency evacuation, which are similar to the location-routing problem (LRP) with uncertain demands. They designed a new multi-graph street network considering prohibited turns and intersection delaysand proposed a hybrid intelligent algorithm, including a GA, an artificial neural network (ANN) and hill climbing to solve the proposed stochastic programming model. The algorithm employs partially matched crossover, route mutation and depot mutation operations. The application of the method in transit evacuation planning is illustrated by a practical example of part of Gulfport, USA. [PERSON] and [PERSON] [49] modeled an inventory routing problem in emergency logistics with fuzzy demands, which are converted to deterministic demands by [PERSON]'s fuzzy number ranking method. They solve the problem using a heuristic algorithm that coordinates inventory control and route optimization for minimizing total cost. Computational result shows that the benefits can be obtained from the integration of inventory and routing. [PERSON] and [PERSON] [50] studied a warehouse LRP with a medium-term economic, a short-term economic and a humanitarian objective function. They developed a \"math-heuristic\" method based on an MIP formulation with a heuristically generated constraint pool. As a subproblem, the multiple-depot, multiple-trip capacitated team orienteering problem is solved. Experimental results show that their approach can evolve the solutions towards the Pareto-optimal front and has performance advantage over the NSGA-II.