Abstract
Multi-objective Evolutionary Algorithms (MOEAs) have been widely used in the transportation sector to provide effective management methods for traffic networks in crowded areas that are very complicated and uncertain. Nevertheless, applying MOEAs alongside transport optimization tasks is time-consuming and therefore, anytime behaviour, which indicates the ability of an algorithm to provide adequate solutions at any running time, is desirable. Moreover, in transportation optimization, small population sizes are unavoidable for scenarios where processing capacities are inadequate but require a response in a short time. Therefore, we propose a multi-objective optimization method (NSGA-LS) to improve anytime behaviour and respond well to small population sizes. A neighbour set discovery strategy is introduced and integrated into the local search algorithm to expand the search area, as a result, increasing the convergence speed of the optimization process. NSGA-LS is compared with NSGA-II and LPNSGA-II in different population sizes and the results illustrate that the method proposed has a greater ability to provide superior solutions compared to NSGA-II and LPNSGA-II.
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References
Ben, Z., Lei, S., Dan, C.: Traffic Intersection signal-planning multi-object optimization based on genetic aglorithm. In: Intelligent Systems and Applications (ISA), 2010 2nd International Workshop, pp. 1–4. IEEE, Wuhan, China (2010)
Segredo, E., Luque, G., Segura, C., Alba, E.: Optimising real-world traffic cycle programs by using evolutionary computation. IEEE Access 7, 43915–43932 (2019). https://doi.org/10.1109/ACCESS.2019.2908562
Xinyou, L., Zhili, L., Shenshen, W.: Multi-objective optimized driving strategy of dual-motor EVs using NSGA-II as a case study and comparison of various intelligent algorithms. Appl. Soft Comput. 111, 107684 (2021)
Tsai, C.W., Teng, T.C., Liao, J.T., Chiang, M.-C.: An effective hybrid-heuristic algorithm for urban traffic light scheduling. Neural Comput. Appl. 33, 17535–17549 (2021). https://doi.org/10.1007/s00521-021-06341-8
Zheng, L., Xue, X., Xu, C., Ran, B.: A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties. Transp. Res. Part B: Methodol. 122, 287–308 (2019)
Shih, P-S., Liu, S., Yu, X.-H.: Ant colony optimization for multi-phase traffic signal control. In: 2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE), pp. 517–521. Beijing, China (2022)
Nguyen, P.T.M., Passow, B.N., Yang, Y.: Improving anytime behaviour for traffic signal control optimization based on NSGA-II and local search. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 4611–4618. IEEE, Vancouver, Canada (2016)
Manuel, L., Thomas, S.: Automatically improving the anytime behaviour of optimization algorithms. Eur. J. Oper. Res. 235(3), 569–582 (2014)
Zhang, X., Tian, Y., Cheng, R., Jin, Y.: An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 19(2), 201–213 (2015)
Li, K., Deb, K., Zhang, Q.: Efficient nondomination level update method for steady-state evolutionary multiobjective optimization. IEEE Trans. Cybern. 47(9), 2838–2849 (2017)
Guangwei, Z., Albert, G., Sherr, L.D.: Optimization of adaptive transit signal priority using parallel genetic algorithm. Tsinghua Sci. Technol. 12(2), 131–140 (2007)
Sanchez-Medina, J.J., Galan-Moreno, M.J., Rubio-Royo, E.: Traffic signal optimization in “La Almozara” district in Saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing. Intell. Transp. Syst. IEEE Trans. 11(1), 132–141 (2010)
Shen, Z., Wang, K., Wang, F.-Y.: GPU-based non-dominated sorting genetic algorithm-II for multi-objective traffic light signalling optimization with agent-based modelling. In: Intelligent Transportation Systems (ITSC), 2013 16th International IEEE Conference on, pp. 1840–1845. IEEE, The Hague, Netherlands (2013)
Laura, B., Daniel, K., AntonioPio, M., Carlo, M., Fabio, C.: Traffic simulation for all: a real world traffic scenario from the City of Bologna. In: Behrisch, M., Weber, M. (eds.) Modeling Mobility with Open Data, pp. 47–60. Springer, Lecture Notes in Mobility (2015)
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This research is funded by Thai Nguyen University of Information and Communication Technology under grant number T2023-07-02.
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Nguyen, P.T.M., Vu, L.H. (2023). Combining Local Search and Multi-objective Optimization Algorithm in Signalized Intersection Optimization. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2023. Lecture Notes in Networks and Systems, vol 847. Springer, Cham. https://doi.org/10.1007/978-3-031-49529-8_24
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DOI: https://doi.org/10.1007/978-3-031-49529-8_24
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