Agent-based evacuation modeling with multiple exits using NeuroEvolution of Augmenting Topologies

Authored by Mehmet Erkan Yuksel

Date Published: 2018

DOI: 10.1016/j.aei.2017.11.003

Sponsors: No sponsors listed

Platforms: Java

Model Documentation: Other Narrative Flow charts Pseudocode

Model Code URLs: Model code not found

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.

Tags

Genetic Algorithms Simulation Agent-Based Modeling and Simulation Pedestrians Evolutionary computation Search Environments Neural-networks Navigation Behaviors Neuroevolution Evacuation modeling Emergency evacuation Human crowds Indoor