[1] 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)

[2] 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

[3] 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)

[4] 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

[5] 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)

[6] 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)

[7] 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)

[8] Manuel, L., Thomas, S.: Automatically improving the anytime behaviour of optimization algorithms. Eur. J. Oper. Res. 235(3), 569–582 (2014)

[9] 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)

[10] 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)

[11] 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)

[12] 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)

[13] 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)

[14] 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)