RT Conference Proceedings
T1 Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments.
A1 Cotta-Porras, Carlos
A1 Gallardo-Ruiz, José Enrique
K1 Algoritmos evolutivos
AB The problem of finding the optimal placement of emergency exits in an indoor environment to facilitate the rapid and orderly evacuation of crowds is addressed in this work. A cellular-automaton model is used to simulate the behavior of pedestrians in such scenarios, taking into account factors such as the environment, the pedestrians themselves, and the interactions among them. A metric is proposed to determine how successful or satisfactory an evacuation was. Subsequently, two metaheuristic algorithms, namely an iterated greedy heuristic and an evolutionary algorithm (EA) are proposed to solve the optimization problem. A comparative analysis shows that the proposed EA is able to find effective solutions for different scenarios, and that an island-based version of it outperforms the other two algorithms in terms of solution quality.
PB Springer Nature
YR 2024
FD 2024
LK https://hdl.handle.net/10630/31431
UL https://hdl.handle.net/10630/31431
LA eng
NO Cotta, C., Gallardo, J.E. (2024). Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14634. Springer, Cham. https://doi.org/10.1007/978-3-031-56852-7_13
NO Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policies
NO Spanish Ministry of Science and Innovation under project Bio4Res (PID2021-125184NB-I00 - http://bio4res.lcc.uma.es) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026