2026-06-02T17:22:55Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/350792026-02-03T12:19:32Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Cotta-Porras, Carlos author 2024 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. 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 https://hdl.handle.net/10630/35079 Computación evolutiva Optimization-Driven Workplace Evacuation using Evolutionary Algorithms and Derivative-Free Methods