References
- Cai, X., Sun, Q., Li, Z., Xiao, Y., Mei, Y., Zhang, Q., & Li, X. (2022). Cooperative coevolution with knowledge-based dynamic variable decomposition for bilevel multiobjective optimization. IEEE Transactions on Evolutionary Computation. Advance online publication. https://doi.org/10.1109/TEVC.2022.3154057
- Cai, Y., Rong, Q., Yang, Z., Yue, W., & Qian, T. (2018). An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty. Journal of Hydrology, 557(2), 713–725. https://doi.org/10.1016/j.jhydrol.2017.12.067
- Cao, Z., Zhou, Y., Yang, A., & Peng, S. (2021). Deep transfer learning mechanism for fine-grained cross-domain sentiment classification. Connection Science, 33(4), 911–928. https://doi.org/10.1080/09540091.2021.1912711
- Chen, Z., Wang, C., Jin, H., Li, J., Zhang, S., & Ouyang, Q. (2022). Hierarchical-fuzzy allocation and multi-parameter adjustment prediction for industrial loading optimisation. Connection Science, 34(1), 687–708. https://doi.org/10.1080/09540091.2022.2031887
- Deb, K., & Sinha, A. (2009). An evolutionary approach for bilevel multi-objective problems. In International conference on multiple criteria decision making (pp. 17–24). Springer.
- Deb, K., & Sinha, A. (2010). An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm. Evolutionary Computation, 18(3), 403–449. https://doi.org/10.1162/EVCO_a_00015
- He, Q., & Lv, Y. (2017). Particle swarm optimization based on smoothing approach for solving a class of bi-level multiobjective programming problem. Cybernetics and Information Technologies, 17(3), 59–74. https://doi.org/10.1515/cait-2017-0030
- Jain, H., & Deb, K. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18(4), 602–622. https://doi.org/10.1109/TEVC.2013.2281534
- Kamal, M., Gupta, S., Chatterjee, P., Pamucar, D., & Stevic, Z. (2019). Bi-level multi-objective production planning problem with multi-choice parameters: A fuzzy goal programming algorithm. Algorithms, 12(7), 143. https://doi.org/10.3390/a12070143.
- Kolak, O. I., Feyzioglu, O., & Noyan, N. (2018). Bi-level multi-objective traffic network optimisation with sustainability perspective. Expert Systems with Applications, 104(August), 294–306. https://doi.org/10.1016/j.eswa.2018.03.034
- Li, Y., Dai, H.-N., & Zheng, Z. (2022). Selective transfer learning with adversarial training for stock movement prediction. Connection Science, 34(1), 492–510. https://doi.org/10.1080/09540091.2021.2021143
- Lin, Y., Pan, S., Jia, L., & Zou, N. (2010). A bi-level multi-objective programming model for bus crew and vehicle scheduling. In 2010 8th world congress on intelligent control and automation (pp. 2328–2333). IEEE.
- Liu, H., Gu, F., & Zhang, Q. (2014). Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems. IEEE Transactions on Evolutionary Computation, 18(3), 450–455. https://doi.org/10.1109/TEVC.4235
- Lv, T., Ai, Q., & Zhao, Y. (2016). A bi-level multi-objective optimal operation of grid-connected microgrids. Electric Power Systems Research, 131(6), 60–70. https://doi.org/10.1016/j.epsr.2015.09.018
- Ma, L., Huang, M., Yang, S., Wang, R., & Wang, X. (2021). An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization. IEEE Transactions on Cybernetics. Advance online publication. https://doi.org/10.1109/TCYB.2020.3041212
- Ni, S., Zhang, L., Wu, G., Shi, P., & Zheng, J. (2020). Multi-objective bi-level optimal dispatch method of active distribution network considering dynamic reconfigurations. In 2020 5th Asia conference on power and electrical engineering (ACPEE) (pp. 2077–2082). IEEE.
- Pieume, C. O., Marcotte, P., Fotso, L. P., & Siarry, P. (2013). Generating efficient solutions in bilevel multi-objective programming problems. American Journal of Operations Research, 3(2), 289–298. https://doi.org/10.4236/ajor.2013.32026
- Ruuska, S., & Miettinen, K. (2012). Constructing evolutionary algorithms for bilevel multiobjective optimization. In 2012 IEEE congress on evolutionary computation (pp. 1–7). IEEE.
- Said, R., Bechikh, S., Louati, A., Aldaej, A., & Said, L. B. (2020). Solving combinatorial multi-objective bi-level optimization problems using multiple populations and migration schemes. IEEE Access, 8(2169-3536), 141674–141695. https://doi.org/10.1109/Access.6287639.
- Shaikh, P. W., El-Abd, M., Khanafer, M., & Gao, K. (2020). A review on swarm intelligence and evolutionary algorithms for solving the traffic signal control problem. IEEE Transactions on Intelligent Transportation Systems, 23(99), 1–16. https://doi.org/10.1109/TITS.2020.3014296
- Sinha, A., Malo, P., & Deb, K. (2017). Approximated set-valued mapping approach for handling multiobjective bilevel problems. Computers & Operations Research, 77(0305-0548), 194–209. https://doi.org/10.1016/j.cor.2016.08.001.
- Sinha, A., Malo, P., Deb, K., Korhonen, P., & Wallenius, J. (2016). Solving bilevel multicriterion optimization problems with lower level decision uncertainty. IEEE Transactions on Evolutionary Computation, 20(2), 199–217. https://doi.org/10.1109/TEVC.2015.2443057
- Song, E., & Li, H. (2022). A hybrid differential evolution for multi-objective optimisation problems. Connection Science, 34(1), 224–253. https://doi.org/10.1080/09540091.2021.1984396
- Sundar, S., & Sumathy, S. (2022). Transfer learning approach in deep neural networks for uterine fibroid detection. International Journal of Computational Science and Engineering, 25(1), 52–63. https://doi.org/10.1504/IJCSE.2022.120788
- Tao, Z., Hu, T., Yue, Z., & Guo, X. (2012). An improved particle swarm optimization for solving bilevel multiobjective programming problem. Journal of Applied Mathematics, 2012(1110-757X), 359–373. https://doi.org/10.1155/2012/626717
- Vesterstrom, J., & Thomsen, R. (2004). A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In Congress on evolutionary computation (pp. 1980–1987).
- Vishwakarma, V. P., & Sisaudia, V. (2020). Self-adjustive de and kelm-based image watermarking in DCT domain using fuzzy entropy. International Journal of Embedded Systems, 13(1), 74–84. https://doi.org/10.1504/IJES.2020.108286
- Yanhong, L., Dong, W., Xiaonian, S., & Zhenzhou, Y. (2011). Research on allocation and optimization of passenger structure within comprehensive transportation corridor based on bi-level multi-objective programming. In Proceedings 2011 international conference on transportation, mechanical, and electrical engineering (TMEE) (pp. 240–247). IEEE.
- Zhang, G., Lu, J., & Dillon, T. (2007). Decentralized multi-objective bilevel decision making with fuzzy demands. Knowledge-Based Systems, 20(5), 495–507. https://doi.org/10.1016/j.knosys.2007.01.003 Intelligent Knowledge Engineering Systems.
- Zhang, Q., & Li, H. (2007). Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731. https://doi.org/10.1109/TEVC.2007.892759
- Zhang, T., Hu, T., Chen, J. W., Wan, Z., & Guo, X. (2012). Solving bilevel multiobjective programming problem by elite quantum behaved particle swarm optimization. Abstract & Applied Analysis, 2012, 97–112. https://doi.org/10.1155/2012/102482
- Zhang, T., Hu, T., Guo, X., Chen, Z., & Zheng, Y. (2013). Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator. Knowledge-Based Systems, 53(0950-7051), 13–19. https://doi.org/10.1016/j.knosys.2013.07.015.
- Zhang, Y., Gong, D.-W., & Cheng, J. (2017). Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(1), 64–75. https://doi.org/10.1109/TCBB.2015.2476796
- Zhang, Z. (2014). A modm bi-level model with fuzzy random coefficients for resource-constrained project scheduling problems. In 2014 seventh international joint conference on computational sciences and optimization (pp. 666–669). IEEE.
- Zhao, F., Du, S., Lu, H., Ma, W., & Song, H. (2021). A hybrid self-adaptive invasive weed algorithm with differential evolution. Connection Science, 33(4), 929–953. https://doi.org/10.1080/09540091.2021.1917517