Hierarchical preference algorithm based on decomposition multiobjective optimization

Swarm and Evolutionary Computation - Tập 60 - Trang 100771 - 2021
Juan Zou1,2, Yongwu He1,2, Jinhua Zheng1,2, Dunwei Gong3, Qite Yang1,2, Liuwei Fu1,2, Tingrui Pei1,2
1Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan, China
2Faculty of Informational Engineering University of Xiangtan, Xiangtan, 411105, China
3School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou 221000, China

Tài liệu tham khảo

Giagkiozis, 2015, An overview of population-based algorithms for multi-objective optimisation, International Journal of Systems Science, 46, 1572, 10.1080/00207721.2013.823526 Deb, 2001, 16 Coello, 2004, 1 Luque, 2020, Adaptive global WASF-GA to handle many-objective optimization problems, Swarm and Evolutionary Computation, 54, 100644, 10.1016/j.swevo.2020.100644 Das, 2019, Evolutionary algorithm using adaptive fuzzy dominance and reference point for many-objective optimization, Swarm and Evolutionary Computation, 44, 1092, 10.1016/j.swevo.2018.11.003 Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182, 10.1109/4235.996017 Zhang, 2007, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11, 712, 10.1109/TEVC.2007.892759 Rui, 2016, Decomposition based algorithms using pareto adaptive scalarizing methods, IEEE Transactions on Evolutionary Computation, 20, 821, 10.1109/TEVC.2016.2521175 Li, 2015, An evolutionary many-objective optimization algorithm based on dominance and decomposition, IEEE Transactions on Evolutionary Computation, 19, 694, 10.1109/TEVC.2014.2373386 Yang, 2019, PBI function based evolutionary algorithm with precise penalty parameter for unconstrained many-objective optimization, Swarm and Evolutionary Computation, 50, 100568, 10.1016/j.swevo.2019.100568 Liu, 2019, A novel multi-objective evolutionary algorithm with dynamic decomposition strategy, Swarm and Evolutionary Computation, 48, 182, 10.1016/j.swevo.2019.02.010 Zitzler, 2004, Indicator-based selection in multiobjective search, 832 Pamulapati, 2018, ISDE+-an indicator for multi and many-objective optimization, IEEE Transactions on Evolutionary Computation, 23, 346, 10.1109/TEVC.2018.2848921 Ikeda, 2001, Failure of pareto-based moeas: Does non-dominated really mean near to optimal?, 2, 957 Khare, 2003, Performance scaling of multi-objective evolutionary algorithms, 376 Purshouse, 2003, Evolutionary many-objective optimisation: An exploratory analysis, 3, 2066 Zou, 2019, A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization, Swarm and Evolutionary Computation, 47, 33, 10.1016/j.swevo.2019.02.001 Zou, 2019, An adaptation reference-point-based multiobjective evolutionary algorithm, Information Sciences, 488, 41, 10.1016/j.ins.2019.03.020 Mardle, 2000, Nonlinear multiobjective optimization, Journal of the Operational Research Society, 51, 246, 10.2307/254267 Hwang, 2012, 164 Xin, 2018, Interactive multiobjective optimization: A review of the state-of-the-art, IEEE Access, 6, 41256, 10.1109/ACCESS.2018.2856832 Branke, 2005, Integrating user preferences into evolutionary multi-objective optimization, 461 Cheng, 2015, Reference vector based a posteriori preference articulation for evolutionary multiobjective optimization, 939 Gong, 2011, Interactive MOEA/D for multi-objective decision making, 721 Branke, 2008, 5252 Molina, 2009, g-dominance: Reference point based dominance for multiobjective metaheuristics, European Journal of Operational Research, 197, 685, 10.1016/j.ejor.2008.07.015 Said, 2010, The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making, IEEE Transactions on Evolutionary Computation, 14, 801, 10.1109/TEVC.2010.2041060 Hu, 2017, A preference-based multi-objective evolutionary algorithm using preference selection radius, Soft Computing, 21, 5025, 10.1007/s00500-016-2099-9 Yu, 2016, Decomposing the user-preference in multiobjective optimization, Soft Computing, 20, 4005, 10.1007/s00500-015-1736-z Li, 2018, Integration of preferences in decomposition multiobjective optimization, IEEE Transactions on Cybernetics, 48, 3359, 10.1109/TCYB.2018.2859363 Wang, 2019, An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization, Swarm and Evolutionary Computation, 49, 220, 10.1016/j.swevo.2019.06.009 Qi, 2019, User-preference based decomposition in MOEA/D without using an ideal point, Swarm and Evolutionary Computation, 44, 597, 10.1016/j.swevo.2018.08.002 Yu, 2019, References or preferences–rethinking many-objective evolutionary optimization, 2410 Koksalan, 2010, An interactive territory defining evolutionary algorithm: iTDEA, IEEE Transactions on Evolutionary Computation, 14, 702, 10.1109/TEVC.2010.2070070 Li, 2018, Multiobjective evolutionary algorithms based on target region preferences, Swarm and Evolutionary Computation, 40, 196, 10.1016/j.swevo.2018.02.006 Das, 1998, Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems, SIAM Journal on Optimization, 8, 631, 10.1137/S1052623496307510 Mohamed, 2018, Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation, Soft Computing, 22, 3215, 10.1007/s00500-017-2777-2 Deb, 1995, Simulated binary crossover for continuous search space, Complex Systems, 9, 115 Deb, 1996, A combined genetic adaptive search (geneas) for engineering design, Computer Science and Informatics, 26, 30 Zitzler, 2000, Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computation, 8, 173, 10.1162/106365600568202 Deb, 2005, Scalable test problems for evolutionary multiobjective optimization, 105 Huband, 2006, A review of multiobjective test problems and a scalable test problem toolkit, IEEE Transactions on Evolutionary Computation, 10, 477, 10.1109/TEVC.2005.861417 Li, 2019, Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties, Swarm and Evolutionary Computation, 46, 104, 10.1016/j.swevo.2019.02.003 Tian, 2017, PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], IEEE Computational Intelligence Magazine, 12, 73, 10.1109/MCI.2017.2742868 Van Veldhuizen, 1998, Evolutionary computation and convergence to a pareto front, 221 Wilcoxon, 1992, Individual comparisons by ranking methods, 196 Yi, 2018, ar-MOEA: A novel preference-based dominance relation for evolutionary multiobjective optimization, IEEE Transactions on Evolutionary Computation, 23, 788, 10.1109/TEVC.2018.2884133