RT Conference Proceedings
T1 Optimization-Driven Workplace Evacuation using Evolutionary Algorithms and Derivative-Free Methods
A1 Cotta-Porras, Carlos
K1 Computación evolutiva
AB We consider the problem of optimizing the evacuation of a workplace environment by deciding the best arrangement of emergency exits. To this end, we consider a simulation-based approach that relies on the use of cellular automata to model the collective behavior of the crowd. In order to obtain problem instances more akin to realistic workplace environments, a problem instance generator based on L-attributed grammars is devised and described in detail. Subsequently, we consider the use of evolutionary algorithms, an iterated greedy heuristic, and Nelder-Mead method to solve the problem. In-depth experimentation is reported. It is shown that the evolutionary algorithm is superior during training and that Nelder-Mead method is also competitive during test, raising interesting prospects for future hybrid strategies.
PB ACM
YR 2024
FD 2024
LK https://hdl.handle.net/10630/35079
UL https://hdl.handle.net/10630/35079
LA eng
NO C. Cotta, Optimization-Driven Workplace Evacuation using Evolutionary Algorithms and Derivative-Free Methods, Proceedings of the 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, pp. 86-91, ACM, 2024
NO Ministerio de Ciencia e Innovación: project Bio4Res (PID2021-125184NB-I00)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026