Evacuation Path Planning Based on the Hybrid Improved Sparrow Search Optimization Algorithm

In the face of fire in buildings, people need to quickly plan their escape routes. Intelligent optimization algorithms can achieve this goal, including the sparrow search algorithm (SSA). Despite the powerful search ability of the SSA, there are still some areas that need improvements. Aiming at the...

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Bibliographic Details
Published in:Fire (Basel, Switzerland) Vol. 6; no. 10; p. 380
Main Authors: Wei, Xiaoge, Zhang, Yuming, Zhao, Yinlong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2023
Subjects:
Adaptive algorithms
adaptive perturbation
Algorithms
chaotic mapping
equations
Evacuation
Evacuation of civilians
evacuation path planning
Evacuation routing
Food
Foraging behavior
hybrids
Intelligence
levy
Logistics
Machine learning
Markov chain
Mathematical optimization
Methods
Normal distribution
Objective function
Optimization
Optimization algorithms
Passeriformes
Path planning
Planning
Search algorithms
Simulation
Smoothness
SSA
Unmanned aerial vehicles
China
ISSN:2571-6255, 2571-6255
Online Access:Get full text
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Summary:In the face of fire in buildings, people need to quickly plan their escape routes. Intelligent optimization algorithms can achieve this goal, including the sparrow search algorithm (SSA). Despite the powerful search ability of the SSA, there are still some areas that need improvements. Aiming at the problem that the sparrow search algorithm reduces population diversity and is easy to fall into local optimum when solving the optimal solution of the objective function, a hybrid improved sparrow search algorithm is proposed. First, logistic-tent mapping is used to initialize the population and enhance diversity in the population. Also, an adaptive period factor is introduced into the producer’s update position equation. Then, the Lévy flight is introduced to the position of the participant to improve the optimization ability of the algorithm. Finally, the adaptive disturbance strategy is adopted for excellent individuals to strengthen the ability of the algorithm to jump out of the local optimum in the later stage. In order to prove the improvement of the optimization ability of the improved algorithm, the improved sparrow algorithm is applied to five kinds of maps for evacuation path planning and compared with the simulation results of other intelligent algorithms. The ultimate simulation results show that the optimization algorithm proposed in this paper has better performance in path length, path smoothness, and algorithm convergence, showing better optimization performance.
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ISSN:2571-6255
2571-6255
DOI:10.3390/fire6100380