Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments

TL;DR

This paper presents an evolutionary algorithm approach to optimize emergency exit placement in indoor environments, using a cellular automaton model to simulate pedestrian evacuation and compare algorithm effectiveness.

Contribution

It introduces an evolutionary algorithm with an island-based variant for optimizing emergency exit placement, outperforming other metaheuristics in evacuation scenarios.

Findings

EA effectively finds optimal exit placements

Island-based EA outperforms other algorithms

Proposed metric assesses evacuation success

Abstract

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.