## 3 Literature Review
However, the tested cases were simplified to produce an exact model, while foraging behaviors are complex. [PERSON] et al. [28] developed a neural network-based pheromone model for swarm foraging. The main purpose of their proposed method was to determine the key parameters for swarm foraging by updating the neural network output based on the robots' pheromones. The reported results showed that the proposed algorithm may face difficulties in large environments. If there are obstacles, the robot might stay in the obstacle-avoidance mode for a long period of time, which means that it is not scalable or adaptable to large or dynamic environments, and further improvements are needed. Nevertheless, bacteria-like behavior is another nature-inspired concept that has been adapted to DTA in swarm foraging. [PERSON] et al. [29] proposed a bacteria-inspired heuristic for DTA in unmanned aerial vehicles (UAVs). The proposed approach was tested with three different heuristics and was found to maintain a steady execution time in different environmental contexts. However, the proposed method is problem-specific, and the tasks were assumed to be independent. The literature discussed above assumes that tasks have equal priority, and deadlines are not considered. However, deadlines are sometimes incorporated into swarm foraging to reflect real-life problems. In this case, the performance of the system is related not only to how fast it can collect food but also to how much food is delivered on time. Thus, methods of swarm foraging have been improved to include temporal constraints. [PERSON] et al. [30] developed a new swarm foraging task with soft deadlines and proposed a mathematical model that can be used to analyze the performance of swarm foraging. Their proposed model allows robots to select their tasks autonomously using a decision matrix, which defines the probability of robot behavior. This probabilistic design was tested in an ARGoS simulator and was found to be efficient when the cost of switching between tasks is neglected. [PERSON] et al. [2] further studied temporal constraints with swarm foraging. In their study, the collected resources were categorized into different types. Although the robots have no prior knowledge about the locations of the targets in the environment, they need to deliver the targets in chronological order depending on their types. A prediction auction-based approach was also proposed, where the robot allocates itself while it executes its tasks. The robot predicts the tasks that need to be performed in the future when it is idle. To test the performance of the proposed approach, the blocks world for teams(BW4T) simulator was used to determine that there was a trade-off between the energy consumption and execution time. An ACO-based algorithm was proposed by [PERSON] [17] to distribute robots into subtasks and finish before a certain deadline. The proposed algorithm was tested on an ARGoS simulator to determine that ACO can be used for foraging tasks with deadline constraints. However, continuous parameter tuning is required to successfully complete the task. [PERSON] and [PERSON] [31] proposed a self-organized DTA strategy to assign time-constrained tasks to swarm robots. A probability allocation matrix was proposed with respect to different specifications, including the robot performance, soft task deadline, and task size. The main goal was to allow each robot to assign itself to a task depending on its deadline. From the ARGoS simulator results, it was found that even though the proposed strategy could adapt to online arrival tasks, it required neglecting the costs of dropping one task to switch to another. Another ACO algorithm was proposed by [PERSON] et al. [9] to efficiently assign time-constrained tasks to swarm robots. The proposed algorithm allows the robots to communicate with other robots and periodically evaluate the quality of the allocation using pheromone trails to reduce the execution time.