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Multiobjective Optimization of Evacuation Routes in Stadium Using Superposed Potential Field Network Based ACO
Author(s) -
Jialiang Kou,
Shengwu Xiong,
Zhixiang Fang,
Xinlu Zong,
Zhong Chen
Publication year - 2013
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2013/369016
Subject(s) - computer science , mathematical optimization , stadium , ant colony optimization algorithms , multi objective optimization , field (mathematics) , optimization algorithm , potential field , algorithm , simulation , mathematics , machine learning , geometry , geophysics , pure mathematics , geology
Multiobjective evacuation routes optimization problem is defined to find out optimal evacuation routes for a group of evacuees under multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a superposed potential field network (SPFN), and we presented SPFN-based ACO algorithm (SPFN-ACO) to solve this problem based on the proposed model. In Wuhan Sports Center case, we compared SPFN-ACO algorithm with HMERP-ACO algorithm and traditional ACO algorithm under three evacuation objectives, namely, total evacuation time, total evacuation route length, and cumulative congestion degree. The experimental results show that SPFN-ACO algorithm has a better performance while comparing with HMERP-ACO algorithm and traditional ACO algorithm for solving multi-objective evacuation routes optimization problem.

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