Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916263861682176 |
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| author | Cotta, Carlos Gallardo, José E. |
| author_facet | Cotta, Carlos Gallardo, José E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18352 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments Cotta, Carlos Gallardo, José E. Neural and Evolutionary Computing 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. |
| title | Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2405.18352 |