Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search
Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and need to be called repeatedly in iterations of MOEAs. As a result, traffic signal timing optimisation process is time-consuming. Anytime behaviour of an algorithm indicates its ability to return as good solutions as possible at any time during its implementation. Therefore, anytime behavior is desirable in traffic signal timing optimisation algorithms. In this study, we propose an optimisation strategy (NSGA-II-LS) to improve anytime behaviour based on NSGAII and local search. To evaluate the validity of the proposed algorithm, the NSGA-II-LS, NSGA-II and MODEA are used to optimize signal durations of an intersection in Andrea Costa scenario. Results of the experiment show that the optimization method proposed in this study has good anytime behaviour in the traffic signal timings optimization problem.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Nguyen, P.T.M., Passow B.N. and Yang, Y. (2016) Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search. 2016 International Joint Conference on Neural Networks (IJCNN),
ISSN : 2161-4407
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes