References
- Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1), 1–122. 10.1561/2200000016.
- Cao, Y., Yu, W., Ren, W., & Chen, G. (2013). An overview of recent progress in the study of distributed multi-agent coordination. IEEE Transactions on Industrial Informatics, 9(1), 427–438. https://doi.org/10.1109/TII.2012.2219061
- Cao, B., Zhao, J., Lv, Z., & Liu, X. (2017). A distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm for large-scale optimization. IEEE Transactions on Industrial Informatics, 13(4):2030–2038. https://doi.org/10.1109/TII.9424
- Chen, J., Li, J., & Xin, B. (2017). DMOEA-εC: Decomposition based multiobjective evolutionary algorithm with the ϵ-constraint framework. IEEE Transactions on Evolutionary Computation, 21(5), 714–730. https://doi.org/10.1109/TEVC.2017.2683489
- Chen, J., & Sayed, A. H. (2013). Distributed pareto optimization via diffusion strategies. IEEE Journal of Selected Topics in Signal Processing, 7(2), 205–220. https://doi.org/10.1109/JSTSP.2013.2246763
- Deb, K. (2005). Multi-objective optimization using evolutionary algorithms. Wiley.
- Ding, Z. (2014). Consensus control of a class of Lipschitz nonlinear systems. International Journal of Control, 87(11), 2372–2382. https://doi.org/10.1080/00207179.2014.921935
- Fazlyab, M., Paternain, S., Preciado, V. M., & Ribeiro, A. (2018). Prediction-correction interior-point method for time-varying convex optimization. IEEE Transactions on Automatic Control, 63(7):1973–1986. https://doi.org/10.1109/TAC.2017.2760256
- Garg, K., Baranwal, M., Hero, A. O., & Panagou, D. (2020). Fixed-time distributed optimization under time-varying communication topology. arXiv:1905.10472.
- Gharesifard, B., & Cortés, J. (2014). Distributed continuous-time convex optimization on weight-balanced digraphs. IEEE Transactions on Automatic Control, 59(3), 781–786. https://doi.org/10.1109/TAC.2013.2278132
- Godsil, C., & Royle, G. (2001). Algebraic graph theory. Springer.
- Johannes, J. (1984). Scalarization in vector optimization. Mathematical Programming, 29(2), 203–218. https://doi.org/10.1007/BF02592221
- Li, Z., & Ding, Z. (2019). Distributed Nash equilibrium searching via fixed-time consensus-based algorithms. In Proceedings of the 2019 IEEE American control conference (pp. 2765–2770).
- Li, Z., & Ding, Z. (2020). Distributed multiobjective optimization for network resource allocation of multiagent systems. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2019.2961475
- Li, Z., Li, Z., & Ding, Z. (2020). Distributed generalized Nash equilibrium seeking and its application to Femtocell networks. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2020.3004635
- Li, S., Ren, S., Yu, Y., Wang, X., Wang, L., & Quan, G. (2012). Profit and penalty aware scheduling for real-time online services. IEEE Transactions on Industrial Informatics, 8(1):78–89. https://doi.org/10.1109/TII.2011.2172447
- Lin, P., Ren, W., & Farrell, J. A. (2017). Distributed continuous-time optimization: Nonuniform gradient gains, finite-time convergence, and convex constraint set. IEEE Transactions on Automatic Control, 62(5), 2239–2253. https://doi.org/10.1109/TAC.2016.2604324
- Liu, W., Wang, Z., Yuan, Y., Zeng, N., Hone, K., & Liu, X. (2019). A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2019.2925015
- Miettinen, K. (2012). Nonlinear multiobjective optimization. Springer Science & Business Media.
- Nedic, A., & Ozdaglar, A. (2009). Distributed subgradient methods for multi-agent optimization. IEEE Transactions on Automatic Control, 54(1), 48–61. https://doi.org/10.1109/TAC.2008.2009515
- Ning, B., Han, Q.-L., & Zuo, Z. (2019a). Distributed optimization for multiagent systems: An edge-based fixed-time consensus approach. IEEE Transactions on Cybernetics, 49(1), 122–132. https://doi.org/10.1109/TCYB.2017.2766762
- Ning, B., Han, Q.-L., & Zuo, Z. (2019b). Practical fixed-time consensus for integrator-type multi-agent systems: A time base generator approach. Automatica, 105:406–414. https://doi.org/10.1016/j.automatica.2019.04.013
- Polyakov, A. (2012). Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Transactions on Automatic Control, 57(8), 2106–2110. https://doi.org/10.1109/TAC.2011.2179869
- Ren, H., Zhou, W., Nakagami, K., Gao, W., & Wu, Q. (2010). Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects. Applied Energy, 87(12), 3642–3651. https://doi.org/10.1016/j.apenergy.2010.06.013
- Shi, X., Wang, Y., Song, S., & Yan, G. (2018). Distributed optimisation for resource allocation with event-triggered communication over general directed topology. International Journal of Systems Science, 49(6), 1119–1130. https://doi.org/10.1080/00207721.2018.1439124
- Song, B., Wang, Z., & Zou, L. (2017). On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cognitive Computation, 9(1), 5–17. https://doi.org/10.1007/s12559-016-9442-4
- Su, W. (2009). Traffic engineering and time-varying convex optimization [PhD dissertation]. The Pennsylvania State University.
- Tran, N.-T., Xiao, J.-W., Wang, Y.-W., & Yang, W. (2017). Distributed optimisation problem with communication delay and external disturbance. International Journal of Systems Science, 48(16), 3530–3541. https://doi.org/10.1080/00207721.2017.1382605
- Wang, J., & Xin, M. (2013). Optimal consensus algorithm integrated with obstacle avoidance. International Journal of Systems Science, 44(1), 166–177. https://doi.org/10.1080/00207721.2011.598960
- Xu, Y. (2014). Optimal distributed charging rate control of plug-in electric vehicles for demand management. IEEE Transactions on Power Systems, 30(3), 1536–1545. https://doi.org/10.1109/TPWRS.2014.2352265
- Yang, S., Liu, Q., & Wang, J. (2018). A collaborative neurodynamic approach to multiple-objective distributed optimization. IEEE Transactions on Neural Networks and Learning Systems, 29(4), 981–992. https://doi.org/10.1109/TNNLS.2017.2652478
- Yang, J., Zhang, G., & Ma, K. (2016). Hierarchical dispatch using two-stage optimisation for electricity markets in smart grid. International Journal of Systems Science, 47(15), 3529–3536. https://doi.org/10.1080/00207721.2015.1090042
- Yi, P., Hong, Y., & Liu, F. (2016). Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems. Automatica, 74, 259–269. https://doi.org/10.1016/j.automatica.2016.08.007
- Zeng, N., Wang, Z., Zhang, H., & Alsaadi, F. E. (2016). A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cognitive Computation, 8(2), 143–152. https://doi.org/10.1007/s12559-016-9396-6
- Zeng, N., Wang, Z., Zhang, H., Kim, K., Li, Y., & Liu, X. (2019). An improved particle filter with a novel hybrid proposal distribution for quantitative analysis of gold immunochromatographic strips. IEEE Transactions on Nanotechnology, 18:819–829. https://doi.org/10.1109/TNANO.7729
- 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
- Zhao, T., & Ding, Z. (2017). Distributed initialization-free cost-optimal charging control of plug-in electric vehicles for demand management. IEEE Transactions on Industrial Informatics, 13(6), 2791–2801. https://doi.org/10.1109/TII.2017.2685422
- Zuo, Z., Han, Q. L., Ning, B., Ge, X., & Zhang, X. M. (2018). An overview of recent advances in fixed-time cooperative control of multiagent systems. IEEE Transactions on Industrial Informatics, 14(6), 2322–2334. https://doi.org/10.1109/TII.9424