Application of Improved Artificial Bee Colony Algorithm Based on Social Force Model in Crowd Evacuation.
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| Title: | Application of Improved Artificial Bee Colony Algorithm Based on Social Force Model in Crowd Evacuation. |
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| Authors: | Qiqi Zhu1 zhuqiqi9908@163.com, Meijuan Li2 454675273@qq.com, Xuebo Chen2 xuebochen@126.com |
| Source: | Engineering Letters. Dec2025, Vol. 33 Issue 12, p5187-5201. 15p. |
| Subjects: | Civilian evacuation, Human behavior models, Bees algorithm, Traffic congestion, Emergency management, Particle swarm optimization, Computer simulation |
| Abstract: | In emergencies, a rapid and ordered evacuation of the crowd is the key to reducing casualties and property damage. However, existing path planning methods still face challenges in balancing path quality and crowd behavior realism. To address this problem, this paper proposes an improved artificial bee colony algorithm (Genetic PSO enhanced ABC (GPABC)) based on the social force model for crowd evacuation simulation. The method aims to be as close as possible to the real evacuation behavior based on which it can guide the actual evacuation process. The GPABC algorithm deeply integrates the crossovermutation mechanism of genetic algorithms and the local search strategy of particle swarm optimization within the artificial bee colony framework. Introduces crossover operations during the employment and observation phases to break the constraints of local optima. In the scout phase, mutation strategies are employed to enhance the diversity of the solution. In addition, it combines the particle swarm optimization method with adaptive inertia weights to improve the balance between global exploration and local development. To enhance the safety and feasibility of the paths, this article designs a weighted fitness function that incorporates factors such as path length, congestion, obstacle conflicts, and path smoothness. The experimental results show that the GPABC algorithm outperforms traditional Artificial Bee Colony(ABC), Ant Colony Optimization(ACO), and Particle Swarm Optimization(PSO) algorithms in typical test functions, demonstrating faster convergence and higher stability. In crowd evacuation simulations, the GPABC path effectively avoids high-density areas, reducing congestion risks and further validating its effectiveness and potential in practical applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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