Abstract
Traffic flow optimization is an important and challenging problem in handling traffic congestion issues in intelligent transportation systems (ITS). As the simulation and prediction of traffic flows are time-consuming, it is inefficient to apply evolution algorithms (EAs) as the optimizer for this problem. To address this problem, this paper aims to introduce surrogate-assisted EAs (SAEAs) to solve the traffic flow optimization problem. We build a traffic flow model based on cellular automata to simulate the real-world traffic and a surrogate-assisted particle swarm algorithm (SA-PSO) is presented to optimize this time-consuming problem. In the proposed algorithm, a surrogate model based on generalized regression neural network (GRNN) is constructed and local search particle swarm algorithm is applied to select best solutions according to the surrogate model. Then candidate solutions are evaluated using the original traffic flow model, and the surrogate model is updated. This search process iterates until the limited number of function evaluations (FEs) are exhausted. Experimental results show that this method is able to maintain a good performance even with only 600 FEs needed.
This work was in part by the National Natural Science Foundation of China under Grants 61976093, in part by the Science and Technology Plan Project of Guangdong Province 2018B050502006, and in part by Guangdong Natural Science Foundation Research Team 2018B030312003.
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Cai, Sw., Zha, Sc., Chen, Wn. (2020). Online Data-Driven Surrogate-Assisted Particle Swarm Optimization for Traffic Flow Optimization. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_5
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