A novel population robustness-based switching response framework for solving dynamic multi-objective problems
In this paper, a novel population robustness-based switching response framework (PR-SRF) is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the popul...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 583; S. 127601 |
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28.05.2024
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| Abstract | In this paper, a novel population robustness-based switching response framework (PR-SRF) is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the population robustness is described by the quantification of how severely the environmental changes affect current population, which is timely graded as three levels of weak, strong, and normal to enable the adaptive switch of three different responses of diversity-enhancement, diversity-maintenance, and the knowledge-transfer, respectively. In this way, associations between the adopted responses and the changing environments are successfully established, thereby facilitating more intelligent decision when handling the dynamic behaviors. According to the benchmark evaluation results, the proposed PR-SRF-DMOA yields better comprehensive performance than several other DMOAs with popular response strategies, and it also outperforms another three DMOAs with hybrid responses, which demonstrates the great competitiveness of our algorithm. In addition, ablation study proves that the proposed PR-SRF can sufficiently exploit the merits of different responses, which effectively alleviates the negative knowledge transfer in extremely fluctuating environments, thereby providing valuable references for the development of evolutionary transfer optimization (ETO) algorithms. |
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| AbstractList | In this paper, a novel population robustness-based switching response framework (PR-SRF) is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the population robustness is described by the quantification of how severely the environmental changes affect current population, which is timely graded as three levels of weak, strong, and normal to enable the adaptive switch of three different responses of diversity-enhancement, diversity-maintenance, and the knowledge-transfer, respectively. In this way, associations between the adopted responses and the changing environments are successfully established, thereby facilitating more intelligent decision when handling the dynamic behaviors. According to the benchmark evaluation results, the proposed PR-SRF-DMOA yields better comprehensive performance than several other DMOAs with popular response strategies, and it also outperforms another three DMOAs with hybrid responses, which demonstrates the great competitiveness of our algorithm. In addition, ablation study proves that the proposed PR-SRF can sufficiently exploit the merits of different responses, which effectively alleviates the negative knowledge transfer in extremely fluctuating environments, thereby providing valuable references for the development of evolutionary transfer optimization (ETO) algorithms. |
| ArticleNumber | 127601 |
| Author | Zeng, Nianyin Hu, Liwei Wu, Peishu Li, Han Fang, Zheng Liu, Haonan |
| Author_xml | – sequence: 1 givenname: Han orcidid: 0000-0003-0276-9756 surname: Li fullname: Li, Han – sequence: 2 givenname: Zheng orcidid: 0000-0002-7858-4080 surname: Fang fullname: Fang, Zheng – sequence: 3 givenname: Liwei orcidid: 0009-0007-0626-8840 surname: Hu fullname: Hu, Liwei – sequence: 4 givenname: Haonan orcidid: 0000-0003-4382-8492 surname: Liu fullname: Liu, Haonan – sequence: 5 givenname: Peishu orcidid: 0000-0001-9891-3809 surname: Wu fullname: Wu, Peishu – sequence: 6 givenname: Nianyin orcidid: 0000-0002-6957-2942 surname: Zeng fullname: Zeng, Nianyin email: zny@xmu.edu.cn |
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| Cites_doi | 10.1016/j.swevo.2011.02.002 10.1109/TEVC.2021.3101697 10.1080/21642583.2022.2042424 10.1109/TEVC.2017.2771451 10.1109/TEVC.2007.892759 10.1080/21642583.2022.2137707 10.1016/j.neucom.2024.127241 10.1016/j.ins.2019.09.016 10.1007/978-3-319-31153-1_20 10.1109/CEC.2018.8477667 10.1080/21642583.2022.2071778 10.1109/TEVC.2020.3004027 10.1109/TCYB.2020.3029748 10.1109/TEVC.2008.920671 10.3390/app8091673 10.1080/00207721.2023.2209873 10.1109/CEC.2014.6900569 10.1016/j.asoc.2017.05.008 10.1016/j.eswa.2021.115237 10.1016/j.asoc.2017.08.004 10.1016/j.ins.2020.07.009 10.1016/j.asoc.2019.105783 10.1080/00207721.2023.2276095 10.1109/TNNLS.2019.2920887 10.1109/TEVC.2017.2669638 10.1080/00207721.2023.2245543 10.1109/TCYB.2013.2245892 10.1007/s00500-013-1085-8 10.1145/1273496.1273521 10.1109/TCYB.2019.2933499 10.1109/TEVC.2004.831456 10.1007/978-3-540-70928-2_62 10.1007/978-3-540-70928-2_60 10.1109/TSMC.2021.3096220 10.1109/CEC.2009.4983004 10.3390/math10122117 10.1080/00207721.2023.2209846 10.1007/s10489-022-03353-2 |
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| Keywords | Evolutionary transfer optimization (ETO) Switching response framework Population robustness Dynamic multi-objective optimization algorithms (DMOAs) |
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| References | W. Dai, Q. Yang, G. Xue, Y. Yu, Boosting for transfer learning, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 193–200. Zhang, Zou, Liu, Ding, Hu (b6) 2023; 54 K. Deb, N. Rao, S. Karthik, Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling, in: Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization, 2007, pp. 803–817. Sun, Ma, Hu, Yang, Cui (b22) 2023; 53 Li, Wang, Lan, Wu, Zeng (b26) 2023 S. Jiang, S. Yang, X. Yao, K. Tan, M. Kaiser, N. Krasnogor, Benchmark problems for CEC2018 competition on dynamic multiobjective optimisation, in: 2018 IEEE Congress on Evolutionary Computation, CEC, 2018, pp. 1–18. Sahmoud, Topcuoglu (b33) 2019; 85 Zhou, Jin an Q. Zhang (b32) 2014; 44 Li, Liu, Lan, Yin, Wu, Yan, Zeng (b11) 2023; 54 Xu, Tan, Zheng, Li (b20) 2018; 8 Liu, Zhan, Gu, Kwong, Lu, Duh, Zhang (b28) 2020; 31 Fang, Liu, Chen, Lauria, Miron, Liu (b14) 2023; 2 A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, E. Tsang, Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization, in: Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization, 2007, pp. 832–846. Wang, Li, Wang (b42) 2022; 10 Haque, Bhurjee, Kumar (b1) 2022; 10 Wang, Liu, Wang, Fadzil, Lauria, Liu (b2) 2023; 2 Shang, Jiao, Ren, Li, Wang (b41) 2014; 18 Song, Li, Cheng, Dong (b4) 2023; 11 Wang, Li (b13) 2022; 10 Liang, Xu, Liu, Tu, Zhu (b8) 2022; 26 Farina, Deb, Amato (b37) 2004; 8 Zheng, Zhou, Hu, Zhang (b10) 2023; 2 Goh, Tan (b38) 2009; 13 Zeng, Wang, Liu, Zhang, Hone, Liu (b15) 2022; 52 Zou, Li, Yang, Bai, Zheng (b21) 2017; 61 Liang, Liang, Wang, Ma, Liu, Zhu (b29) 2022; 52 Zhu, Li, Zhang (b9) 2023; 54 S. Sahmoud, H. Topcuoglu, A memory-based NSGA-II algorithm for dynamic multi-objective optimization problems, in: European Conference on the Applications of Evolutionary Computation, 2016, pp. 296–310. Chen, Li, Yao (b31) 2018; 22 Y. Wang, B. Li, Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment, in: 2009 IEEE Congress on Evolutionary Computation, CEC, 2009, pp. 630–637. R. Azzouz, S. Bechikh, L. Ben Said, A multiple reference point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes, in: 2014 IEEE Congress on Evolutionary Computation, CEC, 2014, pp. 3168–3175. Jiang, Huang, Qiu, Huang, Yen (b30) 2017; 22 Fang, Li, Hu, Zeng (b23) 2024; 574 Li, Wang, Lan, Wu, Zeng (b25) 2023 Zhang, Li (b43) 2008; 11 Jiang, Wang, Hong, Yen (b27) 2020; 25 Ma, Yang, Sun, Hu, Wei (b34) 2021; 545 Lyshevski (b3) 2024; 55 Sun, Zhou, Wang, Zhang (b35) 2021; 184 Zhang, Wang, Yang, Cui (b12) 2022; 10 Ruan, Yu, Zheng, Zou, Yang (b18) 2017; 58 Derrac, Garcia, Molina, Herrera (b44) 2011; 1 Wang, Zhan, Yu, Lin, Zhang, Gu, Zhang (b7) 2020; 50 Zou, Yen, Tang (b24) 2020; 509 Wang, Sun, Ding (b5) 2022; 1 Wang (10.1016/j.neucom.2024.127601_b7) 2020; 50 Fang (10.1016/j.neucom.2024.127601_b14) 2023; 2 Zeng (10.1016/j.neucom.2024.127601_b15) 2022; 52 Derrac (10.1016/j.neucom.2024.127601_b44) 2011; 1 Zou (10.1016/j.neucom.2024.127601_b21) 2017; 61 Zheng (10.1016/j.neucom.2024.127601_b10) 2023; 2 Liang (10.1016/j.neucom.2024.127601_b29) 2022; 52 10.1016/j.neucom.2024.127601_b45 Liang (10.1016/j.neucom.2024.127601_b8) 2022; 26 Li (10.1016/j.neucom.2024.127601_b11) 2023; 54 Jiang (10.1016/j.neucom.2024.127601_b30) 2017; 22 Wang (10.1016/j.neucom.2024.127601_b13) 2022; 10 Wang (10.1016/j.neucom.2024.127601_b2) 2023; 2 Ma (10.1016/j.neucom.2024.127601_b34) 2021; 545 10.1016/j.neucom.2024.127601_b40 Shang (10.1016/j.neucom.2024.127601_b41) 2014; 18 Goh (10.1016/j.neucom.2024.127601_b38) 2009; 13 Chen (10.1016/j.neucom.2024.127601_b31) 2018; 22 Li (10.1016/j.neucom.2024.127601_b26) 2023 Sun (10.1016/j.neucom.2024.127601_b35) 2021; 184 Wang (10.1016/j.neucom.2024.127601_b42) 2022; 10 Lyshevski (10.1016/j.neucom.2024.127601_b3) 2024; 55 Ruan (10.1016/j.neucom.2024.127601_b18) 2017; 58 Zhu (10.1016/j.neucom.2024.127601_b9) 2023; 54 Zou (10.1016/j.neucom.2024.127601_b24) 2020; 509 Zhang (10.1016/j.neucom.2024.127601_b6) 2023; 54 Zhou (10.1016/j.neucom.2024.127601_b32) 2014; 44 10.1016/j.neucom.2024.127601_b16 Song (10.1016/j.neucom.2024.127601_b4) 2023; 11 10.1016/j.neucom.2024.127601_b17 10.1016/j.neucom.2024.127601_b39 Wang (10.1016/j.neucom.2024.127601_b5) 2022; 1 10.1016/j.neucom.2024.127601_b19 Fang (10.1016/j.neucom.2024.127601_b23) 2024; 574 Liu (10.1016/j.neucom.2024.127601_b28) 2020; 31 Zhang (10.1016/j.neucom.2024.127601_b12) 2022; 10 Jiang (10.1016/j.neucom.2024.127601_b27) 2020; 25 Haque (10.1016/j.neucom.2024.127601_b1) 2022; 10 10.1016/j.neucom.2024.127601_b36 Li (10.1016/j.neucom.2024.127601_b25) 2023 Zhang (10.1016/j.neucom.2024.127601_b43) 2008; 11 Xu (10.1016/j.neucom.2024.127601_b20) 2018; 8 Farina (10.1016/j.neucom.2024.127601_b37) 2004; 8 Sun (10.1016/j.neucom.2024.127601_b22) 2023; 53 Sahmoud (10.1016/j.neucom.2024.127601_b33) 2019; 85 |
| References_xml | – volume: 10 start-page: 899 year: 2022 end-page: 909 ident: b1 article-title: Multi-objective non-linear solid transportation problem with fixed charge, budget constraints under uncertain environments publication-title: Syst. Sci. Control Eng. – volume: 55 start-page: 453 year: 2024 end-page: 466 ident: b3 article-title: Analytic design of constrained control laws for nonlinear dynamic systems with symmetric and asymmetric limits publication-title: Internat. J. Systems Sci. – volume: 22 start-page: 501 year: 2017 end-page: 514 ident: b30 article-title: Transfer learning-based dynamic multiobjective optimization algorithms publication-title: IEEE Trans. Evol. Comput. – volume: 22 start-page: 157 year: 2018 end-page: 171 ident: b31 article-title: Dynamic multiobjectives optimization with a changing number of objectives publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 743 year: 2014 end-page: 756 ident: b41 article-title: Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization publication-title: Soft Comput. – volume: 53 start-page: 1115 year: 2023 end-page: 1131 ident: b22 article-title: A two stages prediction strategy for evolutionary dynamic multi-objective optimization publication-title: Appl. Intell. – reference: R. Azzouz, S. Bechikh, L. Ben Said, A multiple reference point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes, in: 2014 IEEE Congress on Evolutionary Computation, CEC, 2014, pp. 3168–3175. – volume: 44 start-page: 40 year: 2014 end-page: 53 ident: b32 article-title: A population prediction strategy for evolutionary dynamic multiobjective optimization publication-title: IEEE Trans. Cybern. – volume: 11 start-page: 712 year: 2008 end-page: 731 ident: b43 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – year: 2023 ident: b25 article-title: A novel dynamic multiobjective optimization algorithm with hierarchical response system publication-title: IEEE Trans. Comput. Soc. Syst. – volume: 54 start-page: 1855 year: 2023 end-page: 1872 ident: b6 article-title: A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities publication-title: Internat. J. Systems Sci. – volume: 58 start-page: 631 year: 2017 end-page: 647 ident: b18 article-title: The effect of diversity maintenance on prediction in dynamic multi-objective optimization publication-title: Appl. Soft Comput. – volume: 85 year: 2019 ident: b33 article-title: Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms publication-title: Appl. Soft Comput. – reference: Y. Wang, B. Li, Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment, in: 2009 IEEE Congress on Evolutionary Computation, CEC, 2009, pp. 630–637. – volume: 545 start-page: 1 year: 2021 end-page: 24 ident: b34 article-title: Multiregional co-evolutionary algorithm for dynamic multiobjective optimization publication-title: Inform. Sci. – volume: 574 year: 2024 ident: b23 article-title: A learnable population filter for dynamic multi-objective optimization publication-title: Neurocomputing – volume: 31 start-page: 1557 year: 2020 end-page: 1570 ident: b28 article-title: Neural network-based information transfer for dynamic optimization publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 1 start-page: 3 year: 2011 end-page: 18 ident: b44 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. – reference: K. Deb, N. Rao, S. Karthik, Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling, in: Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization, 2007, pp. 803–817. – reference: S. Sahmoud, H. Topcuoglu, A memory-based NSGA-II algorithm for dynamic multi-objective optimization problems, in: European Conference on the Applications of Evolutionary Computation, 2016, pp. 296–310. – volume: 8 start-page: 1673 year: 2018 ident: b20 article-title: Memory-enhanced dynamic multi-objective evolutionary algorithm based on publication-title: Appl. Sci. – volume: 1 start-page: 85 year: 2022 end-page: 98 ident: b5 article-title: Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques publication-title: Int. J. Netw. Dyn. Intell. – reference: W. Dai, Q. Yang, G. Xue, Y. Yu, Boosting for transfer learning, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 193–200. – volume: 8 start-page: 425 year: 2004 end-page: 442 ident: b37 article-title: Dynamic multiobjective optimization problems: test cases, approximations, and applications publication-title: IEEE Trans. Evol. Comput. – year: 2023 ident: b26 article-title: A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 13 start-page: 103 year: 2009 end-page: 127 ident: b38 article-title: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 184 year: 2021 ident: b35 article-title: A new PC-PSO algorithm for Bayesian network structure learning with structure priors publication-title: Expert Syst. Appl. – volume: 11 year: 2023 ident: b4 article-title: An improved dynamic programming tracking-before-detection algorithm based on LSTM network value function publication-title: Syst. Sci. Control Eng. – volume: 25 start-page: 117 year: 2020 end-page: 129 ident: b27 article-title: Knee point-based imbalanced transfer learning for dynamic multiobjective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 52 start-page: 4457 year: 2022 end-page: 4469 ident: b29 article-title: Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution publication-title: IEEE Trans. Syst. Man Cybern.: Syst. – reference: S. Jiang, S. Yang, X. Yao, K. Tan, M. Kaiser, N. Krasnogor, Benchmark problems for CEC2018 competition on dynamic multiobjective optimisation, in: 2018 IEEE Congress on Evolutionary Computation, CEC, 2018, pp. 1–18. – volume: 2 start-page: 24 year: 2023 end-page: 50 ident: b14 article-title: A survey of algorithms, applications and trends for particle swarm optimization publication-title: Int. J. Netw. Dyn. Intell. – volume: 509 start-page: 193 year: 2020 end-page: 209 ident: b24 article-title: A knee-guided prediction approach for dynamic multi-objective optimization publication-title: Inform. Sci. – volume: 52 start-page: 9290 year: 2022 end-page: 9301 ident: b15 article-title: A dynamic neighborhood-based switching particle swarm optimization algorithm publication-title: IEEE Trans. Cybern. – volume: 2 year: 2023 ident: b2 article-title: A novel multi-objective optimization approach with flexible operation planning strategy for truck scheduling publication-title: Int. J. Netw. Dyn. Intell. – volume: 54 start-page: 2590 year: 2023 end-page: 2607 ident: b9 article-title: Model-free robust decoupling control of nonlinear nonaffine dynamic systems publication-title: Internat. J. Systems Sci. – reference: A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, E. Tsang, Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization, in: Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization, 2007, pp. 832–846. – volume: 50 start-page: 2715 year: 2020 end-page: 2729 ident: b7 article-title: Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling publication-title: IEEE Trans. Cybern. – volume: 54 start-page: 1713 year: 2023 end-page: 1728 ident: b11 article-title: SMWO/D: a decomposition-based switching multi-objective whale optimiser for structural optimisation of turbine disk in aero-engines publication-title: Internat. J. Systems Sci. – volume: 2 year: 2023 ident: b10 article-title: Dynamic scheduling for large-scale flexible job shop based on noisy DDQN publication-title: Int. J. Netw. Dyn. Intell. – volume: 10 start-page: 488 year: 2022 end-page: 495 ident: b13 article-title: Theoretical analysis of garden balsam optimization algorithm publication-title: Syst. Sci. Control Eng. – volume: 26 start-page: 319 year: 2022 end-page: 333 ident: b8 article-title: Evolutionary many-task optimization based on multisource knowledge transfer publication-title: IEEE Trans. Evol. Comput. – volume: 61 start-page: 806 year: 2017 end-page: 818 ident: b21 article-title: A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization publication-title: Appl. Soft Comput. – volume: 10 start-page: 2117 year: 2022 ident: b42 article-title: Combining key-points-based transfer learning and hybrid prediction strategies for dynamic multi-objective optimization publication-title: Mathematics – volume: 10 start-page: 115 year: 2022 end-page: 125 ident: b12 article-title: Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation publication-title: Syst. Sci. Control Eng. – volume: 1 start-page: 3 issue: 1 year: 2011 ident: 10.1016/j.neucom.2024.127601_b44 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 – volume: 26 start-page: 319 issue: 2 year: 2022 ident: 10.1016/j.neucom.2024.127601_b8 article-title: Evolutionary many-task optimization based on multisource knowledge transfer publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2021.3101697 – volume: 10 start-page: 115 issue: 1 year: 2022 ident: 10.1016/j.neucom.2024.127601_b12 article-title: Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2022.2042424 – volume: 22 start-page: 501 issue: 4 year: 2017 ident: 10.1016/j.neucom.2024.127601_b30 article-title: Transfer learning-based dynamic multiobjective optimization algorithms publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2771451 – volume: 11 start-page: 712 issue: 6 year: 2008 ident: 10.1016/j.neucom.2024.127601_b43 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 10 start-page: 899 issue: 1 year: 2022 ident: 10.1016/j.neucom.2024.127601_b1 article-title: Multi-objective non-linear solid transportation problem with fixed charge, budget constraints under uncertain environments publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2022.2137707 – volume: 574 year: 2024 ident: 10.1016/j.neucom.2024.127601_b23 article-title: A learnable population filter for dynamic multi-objective optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.127241 – volume: 509 start-page: 193 year: 2020 ident: 10.1016/j.neucom.2024.127601_b24 article-title: A knee-guided prediction approach for dynamic multi-objective optimization publication-title: Inform. Sci. doi: 10.1016/j.ins.2019.09.016 – volume: 2 start-page: 24 issue: 1 year: 2023 ident: 10.1016/j.neucom.2024.127601_b14 article-title: A survey of algorithms, applications and trends for particle swarm optimization publication-title: Int. J. Netw. Dyn. Intell. – ident: 10.1016/j.neucom.2024.127601_b19 doi: 10.1007/978-3-319-31153-1_20 – ident: 10.1016/j.neucom.2024.127601_b39 doi: 10.1109/CEC.2018.8477667 – volume: 10 start-page: 488 issue: 1 year: 2022 ident: 10.1016/j.neucom.2024.127601_b13 article-title: Theoretical analysis of garden balsam optimization algorithm publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2022.2071778 – volume: 25 start-page: 117 issue: 1 year: 2020 ident: 10.1016/j.neucom.2024.127601_b27 article-title: Knee point-based imbalanced transfer learning for dynamic multiobjective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2020.3004027 – volume: 52 start-page: 9290 issue: 9 year: 2022 ident: 10.1016/j.neucom.2024.127601_b15 article-title: A dynamic neighborhood-based switching particle swarm optimization algorithm publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.3029748 – volume: 13 start-page: 103 issue: 1 year: 2009 ident: 10.1016/j.neucom.2024.127601_b38 article-title: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.920671 – volume: 2 issue: 2 year: 2023 ident: 10.1016/j.neucom.2024.127601_b2 article-title: A novel multi-objective optimization approach with flexible operation planning strategy for truck scheduling publication-title: Int. J. Netw. Dyn. Intell. – volume: 8 start-page: 1673 issue: 9 year: 2018 ident: 10.1016/j.neucom.2024.127601_b20 article-title: Memory-enhanced dynamic multi-objective evolutionary algorithm based on Lp decomposition publication-title: Appl. Sci. doi: 10.3390/app8091673 – volume: 2 issue: 4 year: 2023 ident: 10.1016/j.neucom.2024.127601_b10 article-title: Dynamic scheduling for large-scale flexible job shop based on noisy DDQN publication-title: Int. J. Netw. Dyn. Intell. – volume: 54 start-page: 1713 issue: 8 year: 2023 ident: 10.1016/j.neucom.2024.127601_b11 article-title: SMWO/D: a decomposition-based switching multi-objective whale optimiser for structural optimisation of turbine disk in aero-engines publication-title: Internat. J. Systems Sci. doi: 10.1080/00207721.2023.2209873 – ident: 10.1016/j.neucom.2024.127601_b17 doi: 10.1109/CEC.2014.6900569 – volume: 58 start-page: 631 year: 2017 ident: 10.1016/j.neucom.2024.127601_b18 article-title: The effect of diversity maintenance on prediction in dynamic multi-objective optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.05.008 – year: 2023 ident: 10.1016/j.neucom.2024.127601_b26 article-title: A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 184 year: 2021 ident: 10.1016/j.neucom.2024.127601_b35 article-title: A new PC-PSO algorithm for Bayesian network structure learning with structure priors publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115237 – volume: 61 start-page: 806 year: 2017 ident: 10.1016/j.neucom.2024.127601_b21 article-title: A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.08.004 – volume: 545 start-page: 1 year: 2021 ident: 10.1016/j.neucom.2024.127601_b34 article-title: Multiregional co-evolutionary algorithm for dynamic multiobjective optimization publication-title: Inform. Sci. doi: 10.1016/j.ins.2020.07.009 – volume: 85 year: 2019 ident: 10.1016/j.neucom.2024.127601_b33 article-title: Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105783 – volume: 55 start-page: 453 issue: 3 year: 2024 ident: 10.1016/j.neucom.2024.127601_b3 article-title: Analytic design of constrained control laws for nonlinear dynamic systems with symmetric and asymmetric limits publication-title: Internat. J. Systems Sci. doi: 10.1080/00207721.2023.2276095 – volume: 31 start-page: 1557 issue: 5 year: 2020 ident: 10.1016/j.neucom.2024.127601_b28 article-title: Neural network-based information transfer for dynamic optimization publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2019.2920887 – volume: 22 start-page: 157 issue: 1 year: 2018 ident: 10.1016/j.neucom.2024.127601_b31 article-title: Dynamic multiobjectives optimization with a changing number of objectives publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2669638 – volume: 54 start-page: 2590 issue: 13 year: 2023 ident: 10.1016/j.neucom.2024.127601_b9 article-title: Model-free robust decoupling control of nonlinear nonaffine dynamic systems publication-title: Internat. J. Systems Sci. doi: 10.1080/00207721.2023.2245543 – volume: 44 start-page: 40 issue: 1 year: 2014 ident: 10.1016/j.neucom.2024.127601_b32 article-title: A population prediction strategy for evolutionary dynamic multiobjective optimization publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2013.2245892 – volume: 18 start-page: 743 issue: 4 year: 2014 ident: 10.1016/j.neucom.2024.127601_b41 article-title: Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization publication-title: Soft Comput. doi: 10.1007/s00500-013-1085-8 – volume: 11 issue: 1 year: 2023 ident: 10.1016/j.neucom.2024.127601_b4 article-title: An improved dynamic programming tracking-before-detection algorithm based on LSTM network value function publication-title: Syst. Sci. Control Eng. – ident: 10.1016/j.neucom.2024.127601_b36 doi: 10.1145/1273496.1273521 – volume: 50 start-page: 2715 issue: 6 year: 2020 ident: 10.1016/j.neucom.2024.127601_b7 article-title: Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2933499 – volume: 1 start-page: 85 issue: 1 year: 2022 ident: 10.1016/j.neucom.2024.127601_b5 article-title: Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques publication-title: Int. J. Netw. Dyn. Intell. – volume: 8 start-page: 425 issue: 5 year: 2004 ident: 10.1016/j.neucom.2024.127601_b37 article-title: Dynamic multiobjective optimization problems: test cases, approximations, and applications publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.831456 – ident: 10.1016/j.neucom.2024.127601_b40 doi: 10.1007/978-3-540-70928-2_62 – ident: 10.1016/j.neucom.2024.127601_b16 doi: 10.1007/978-3-540-70928-2_60 – year: 2023 ident: 10.1016/j.neucom.2024.127601_b25 article-title: A novel dynamic multiobjective optimization algorithm with hierarchical response system publication-title: IEEE Trans. Comput. Soc. Syst. – volume: 52 start-page: 4457 issue: 7 year: 2022 ident: 10.1016/j.neucom.2024.127601_b29 article-title: Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution publication-title: IEEE Trans. Syst. Man Cybern.: Syst. doi: 10.1109/TSMC.2021.3096220 – ident: 10.1016/j.neucom.2024.127601_b45 doi: 10.1109/CEC.2009.4983004 – volume: 10 start-page: 2117 issue: 12 year: 2022 ident: 10.1016/j.neucom.2024.127601_b42 article-title: Combining key-points-based transfer learning and hybrid prediction strategies for dynamic multi-objective optimization publication-title: Mathematics doi: 10.3390/math10122117 – volume: 54 start-page: 1855 issue: 8 year: 2023 ident: 10.1016/j.neucom.2024.127601_b6 article-title: A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities publication-title: Internat. J. Systems Sci. doi: 10.1080/00207721.2023.2209846 – volume: 53 start-page: 1115 year: 2023 ident: 10.1016/j.neucom.2024.127601_b22 article-title: A two stages prediction strategy for evolutionary dynamic multi-objective optimization publication-title: Appl. Intell. doi: 10.1007/s10489-022-03353-2 |
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