Agent-based evacuation modeling with multiple exits using NeuroEvolution of Augmenting Topologies. (January 2018)

• Record Type: Journal Article
• Authors: Yuksel, Mehmet Erkan
• Is Part Of: Advanced engineering informatics. Volume 35(2018)
• Page Start: 30
• Page End: 55
• Publication Date: 2018-01
• Subjects: Evolutionary computation -- Neuroevolution -- Genetic algorithms -- Agent-based modeling and simulation -- Evacuation modeling -- Emergency evacuation; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2017.11.003
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Evacuation modeling offers challenging research topics to solve problems related to the development of emergency planning strategies. In this paper, we built an agent-based evacuation simulation model to study the pedestrian dynamics and learning process by applying the NeuroEvolution of Augmenting Topologies (NEAT) which is a powerful method to evolve artificial neural networks (ANNs) through genetic algorithms (GAs). The NEAT method strengthens the analogy between GAs and biological evolution by both optimizing and complexifying the solutions simultaneously. We set our main goal to develop a model by identifying the most appropriate fitness function for the agents that can learn how to change and improve their behaviors in a simulation environment such as moving towards the visible targets, producing efficient locomotion, communicating with each other, and avoiding obstacles while reaching targets. The fitness function we chose captured the learning process effectively and our NEAT-based implementation evolved suitable structures for the ANNs autonomously. According to our experiments and observations in the simulated environment, the agents accomplished their tasks successfully and found their ways to the exits.