TY - GEN
T1 - Crowd control with swarm intelligence
AU - Lin, Ying Yin
AU - Chen, Ying-Ping
PY - 2007/12/1
Y1 - 2007/12/1
N2 - This paper presents a uniform conceptual model based on the particle swarm optimization (PSO) paradigm to simulate crowds in computer graphics. According to the mechanisms of PSO, each person (particle) in the crowd (swarm) can adopt the information to search a path from the initial position to the specified target (optimum) automatically. However, PSO aims to obtain the optimal solution, while the purpose of this study concentrates on the generated paths of particles. Hence, in order to generate appropriate paths of people in a crowd, we propose a method to employ the computational facilities provided in PSO. The proposed model is simple, uniform, and easy to implement. The results of simulations demonstrate that using PSO with the proposed technique can generate appropriate non-deterministic, non-colliding paths in several different scenarios, including static obstacles, moving targets, and multiple crowds.
AB - This paper presents a uniform conceptual model based on the particle swarm optimization (PSO) paradigm to simulate crowds in computer graphics. According to the mechanisms of PSO, each person (particle) in the crowd (swarm) can adopt the information to search a path from the initial position to the specified target (optimum) automatically. However, PSO aims to obtain the optimal solution, while the purpose of this study concentrates on the generated paths of particles. Hence, in order to generate appropriate paths of people in a crowd, we propose a method to employ the computational facilities provided in PSO. The proposed model is simple, uniform, and easy to implement. The results of simulations demonstrate that using PSO with the proposed technique can generate appropriate non-deterministic, non-colliding paths in several different scenarios, including static obstacles, moving targets, and multiple crowds.
UR - http://www.scopus.com/inward/record.url?scp=77649271850&partnerID=8YFLogxK
U2 - 10.1109/CEC.2007.4424900
DO - 10.1109/CEC.2007.4424900
M3 - Conference contribution
AN - SCOPUS:77649271850
SN - 1424413400
SN - 9781424413409
T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
SP - 3321
EP - 3328
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -