Abstract
In many real-world optimization problems, like the traffic light scheduling problem tackled here, the evaluation of candidate solutions requires the simulation of a process under various scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has revealed the effectiveness of IRACE for this task. However, the operators used by IRACE to generate new solutions were designed for configuring algorithmic parameters, that have various data types (categorical, numerical, etc.). Meanwhile, evolutionary algorithms have powerful operators for numerical optimization, which could help to sample new solutions from the best ones found in the search. Therefore, in this work, we propose a hybridization of the elitist iterated racing mechanism of IRACE with evolutionary operators from differential evolution and genetic algorithms. We consider a realistic case study derived from the traffic network of Malaga (Spain) with 275 traffic lights that should be scheduled optimally. After a meticulous study, we discovered that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than conventional algorithms and also improves travel times and reduces pollution.
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- 1.
Legal and technical limitations may make real-time traffic light control infeasible.
- 2.
The source code is available at https://github.com/NEO-Research-Group/irace-ea.
- 3.
Available at https://cran.r-project.org/package=irace.
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Acknowledgements
This research was partially funded by the University of Málaga, Andalucía Tech and the project TAILOR Grant #952215, H2020-ICT-2019-3. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO. M. López-Ibáñez is a “Beatriz Galindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Ministry of Science and Innovation of the Spanish Government. J. Ferrer is supported by a postdoc grant (DOC/00488) funded by the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities.
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Cintrano, C., Ferrer, J., López-Ibáñez, M., Alba, E. (2021). Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs. In: Zarges, C., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2021. Lecture Notes in Computer Science(), vol 12692. Springer, Cham. https://doi.org/10.1007/978-3-030-72904-2_2
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