A random benchmark suite and a new reaction strategy in dynamic multiobjective optimization
Tài liệu tham khảo
Nguyen, 2012, Evolutionary dynamic optimization: a survey of the state of the art, Swarm Evol. Comput., 6, 1, 10.1016/j.swevo.2012.05.001
Branke, 2012, 3
Deb, 2007, Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling, 803
Nguyen, 2014, Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming, IEEE Trans. Evol. Comput., 18, 193, 10.1109/TEVC.2013.2248159
Trivedi, 2015, Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem, Swarm Evol. Comput., 23, 50, 10.1016/j.swevo.2015.04.001
Zhang, 2008, Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control, Appl. Soft Comput., 8, 959, 10.1016/j.asoc.2007.07.005
Wu, 2011, Multi-objective four-dimensional vehicle motion planning in large dynamic environments, IEEE Trans. Syst. Man Cybern. Part B, 41, 621, 10.1109/TSMCB.2010.2061225
Bui, 2012, Adaptation in dynamic environments: a case study in mission planning, IEEE Trans. Evol. Comput., 16, 190, 10.1109/TEVC.2010.2104156
Salomon, 2014, Active robust optimization: enhancing robustness to uncertain environments, IEEE Trans. Cybern., 44, 2221, 10.1109/TCYB.2014.2304475
Kong, 2013, A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing, Appl. Soft Comput., 13, 2960, 10.1016/j.asoc.2012.02.025
Shang, 2014, A multi-population cooperative coevolutionary algorithm for multi-objective capacitated arc routing problem, Inf. Sci., 277, 609, 10.1016/j.ins.2014.03.008
Farina, 2004, Dynamic multiobjective optimization problems: test cases, approximations, and applications, IEEE Trans. Evol. Comput., 8, 425, 10.1109/TEVC.2004.831456
Liu, 2006, New evolutionary algorithm for dynamic multiobjective optimization problems, 889
Goh, 2009, A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization, IEEE Trans. Evol. Comput., 13, 103, 10.1109/TEVC.2008.920671
Wang, 2009, Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment, 630
Vinek, 2011, A dynamic multi-objective optimization framework for selecting distributed deployments in a heterogeneous environment, Procedia Comput. Sci., 4, 166, 10.1016/j.procs.2011.04.018
Hatzakis, 2006, Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach, 1201
Zhou, 2007, Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization, 832
Koo, 2010, A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment, Memet. Comput., 2, 87, 10.1007/s12293-009-0026-7
Muruganantham, 2013, Dynamic multiobjective optimization using evolutionary algorithm with Kalman filter, Procedia Comput. Sci., 24, 66, 10.1016/j.procs.2013.10.028
Zhou, 2014, A population prediction strategy for evolutionary dynamic multiobjective optimization, IEEE Trans. Cybern., 44, 40, 10.1109/TCYB.2013.2245892
Peng, 2015, Novel prediction and memory strategies for dynamic multiobjective optimization, Soft Comput., 19, 2633, 10.1007/s00500-014-1433-3
Liu, 2014, A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model, Soft Comput., 18, 1913, 10.1007/s00500-013-1175-7
Liu, 2015, Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm, Appl. Intell., 43, 192, 10.1007/s10489-014-0625-y
Wu, 2015, A directed search strategy for evolutionary dynamic multiobjective optimization, Soft Comput., 19, 3221, 10.1007/s00500-014-1477-4
Wang, 2020, An ensemble learning based prediction strategy for dynamic multi-objective optimization, Appl. Soft Comput., 96, 106592, 10.1016/j.asoc.2020.106592
Ou, 2019, A pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization, Appl. Soft Comput., 105673, 10.1016/j.asoc.2019.105673
M.S. Lechuga, Multi-objective optimisation using sharing in swarm optimisation algorithms. University of Birmingham.
Helbig, 2012, Analyses of guide update approaches for vector evaluated particle swarm optimisation on dynamic multi-objective optimisation problems, 1
Ma, 2011, A hybrid dynamic multi-objective immune optimization algorithm using prediction strategy and improved differential evolution crossover operator, 435
Shang, 2014, Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization, Soft Comput., 18, 743, 10.1007/s00500-013-1085-8
Jin, 2004, Constructing dynamic optimization test problems using the multi-objective optimization concept, 525
J. Mehnen, G. Rudolph, T. Wagner, Evolutionary optimization of dynamic multiobjective functions.
Huang, 2011, Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants, Inf. Sci., 181, 2370, 10.1016/j.ins.2010.12.015
Biswas, 2014, Evolutionary multiobjective optimization in dynamic environments: a set of novel benchmark functions, 3192
Li, 2009, Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Trans. Evol. Comput., 13, 284, 10.1109/TEVC.2008.925798
Helbig, 2014, Benchmarks for dynamic multi-objective optimisation algorithms, ACM Comput. Surv., 46, 1, 10.1145/2517649
Jiang, 2017, Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons, IEEE Trans. Cybern., 47, 198, 10.1109/TCYB.2015.2510698
Gee, 2017, A benchmark test suite for dynamic evolutionary multiobjective optimization, IEEE Trans. Cybern., 47, 461
Nimmegeers, 2016, Dynamic optimization of biological networks under parametric uncertainty, BMC systems biology, 10, 86, 10.1186/s12918-016-0328-6
Huband, 2006, A review of multiobjective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., 10, 477, 10.1109/TEVC.2005.861417
Schaffer, 1985, Multiple objective optimization with vector evaluated genetic algorithms, 93
Chen, 2017, Dynamic multiobjectives optimization with a changing number of objectives, IEEE Trans. Evol. Comput., 22, 157, 10.1109/TEVC.2017.2669638
Huband, 2006, A review of multiobjective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., 10, 477, 10.1109/TEVC.2005.861417
Zitzler, 2000, Comparison of multiobjective evolutionary algorithms: empirical results, Evol. Comput., 8, 173, 10.1162/106365600568202
Deb, 2005
Q. Zhang, A. Zhou, S. Zhao, P.N. Suganthan, W. Liu, S. Tiwari, Multiobjective optimization test instances for the CEC 2009 special session and competition, university of essex, Colchester, UK and nanyang technological university, singapore, special session on performance assessment of multi-objective optimization algorithms, 2008. technical report, 1–30
Jiang, 2014, A benchmark generator for dynamic multi-objective optimization problems, 1
Eaton, 2017, Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling, IEEE Trans. Intell. Transp. Syst., 18, 2980, 10.1109/TITS.2017.2665042
Xiong, 2017, A multi-objective approach for weapon selection and planning problems in dynamic environments, J. Ind. Manag. Optim., 13, 1189, 10.3934/jimo.2016068
Wang, 2019, An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals, Swarm Evol. Comput., 51, 100594, 10.1016/j.swevo.2019.100594
Ding, 2018, Dynamic evolutionary multiobjective optimization for raw ore allocation in mineral processing, IEEE Trans. Emerg. Top.Comput. Intell., 3, 36
Abello, 2014
R.K. Ursem, T. Krink, B. Filipic, A numerical simulator of a crop-producing greenhouse. EVALife TR No. 2002-01 2002 (1).
J. Butans, Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches.
Ruan, 2017, The effect of diversity maintenance on prediction in dynamic multi-objective optimization, Appl. Soft Comput., 58, 631, 10.1016/j.asoc.2017.05.008
Y. Hu, J. Ou, J. Zheng, J. Zou, S. Yang, G. Ruan, Solving dynamic multi-objective problems with an evolutionary multi-directional search approach, in: Knowledge-Based Systems.
Jiang, 2017, Transfer learning-based dynamic multiobjective optimization algorithms, IEEE Trans. Evol. Comput., 22, 501, 10.1109/TEVC.2017.2771451
Ruan, 2019, When and how to transfer knowledge in dynamic multi-objective optimization, 2034
Ruan, 2020, Computational study on effectiveness of knowledge transfer in dynamic multi-objective optimization, 1
Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017
S. Jiang, S. Yang, A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization, in: IEEE Transactions on Evolutionary Computation.
Zhang, 2007, Moea/d: A multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 712, 10.1109/TEVC.2007.892759
Y. Hu, J. Zheng, J. Zou, S. Yang, J. Ou, R. Wang, A dynamic multi-objective evolutionary algorithm based on intensity of environmental change, in: Information Sciences.
Helbig, 2013, Performance measures for dynamic multi-objective optimisation algorithms, Inf. Sci., 250, 61, 10.1016/j.ins.2013.06.051
Schott, 1995, Fault tolerant design using single and multicriteria genetic algorithm optimization, Cell. Immunol., 37, 1
Carrasco, 2020, Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review, Swarm Evol. Comput., 54, 100665, 10.1016/j.swevo.2020.100665
Zhang, 2008, Rm-meda: a regularity model-based multiobjective estimation of distribution algorithm, Evol. Comput. IEEE Trans., 12, 41, 10.1109/TEVC.2007.894202
Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1