Matlab中platEMO里多目标进化算法对应的参考文献

1.AGE-II

M. Wagner and F. Neumann, A fast approximation-guided evolutionary
multi-objective algorithm, Proceedings of the 15th Annual Conference on
Genetic and Evolutionary Computation, 2013, 687-694.一种快速近似引导的进化多目标算法

2.AGE-MOEA

A. Panichella, An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization, Proceedings of the Genetic and Evolutionary Computation Conference, 2019.一种基于非欧几里德几何的多目标优化自适应进化算法

3.A-NSGA—III

H. Jain and K. Deb, An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part II:Handling constraints and extending to an adaptive approach, IEEE Transactions on Evolutionary Computation, 2014, 18(4): 602-622.一种基于参考点的非支配排序方法的进化多目标优化算法，第二部分：处理约束并扩展到自适应方法

4.ARMOEA

An indicator-based multiobjective evolutionary algorithm with reference point adaptation for better versatility. IEEE Transactions on Evolutionary Computation, 2018, 22(4): 609-622.一种基于指标并且具有参考点好的通用性的多目标进化算法。

5.BCE-IBEA

M. Li, S. Yang, and X. Liu, Pareto or non-Pareto: Bi-criterion evolution in multiobjective optimization, IEEE Transactions on Evolutionary
Computation, 2016, 20(5): 645-665.帕累托或非帕累托：多目标优化中的双准则演化

6.BCE-MOEA-D

M. Li, S. Yang, and X. Liu, Pareto or non-Pareto: Bi-criterion evolution in multiobjective optimization, IEEE Transactions on Evolutionary Computation, 2016, 20(5): 645-665.帕累托或非帕累托：多目标优化中的双准则演化

7.BiGE M.

Li, S. Yang, and X. Liu, Bi-goal evolution for many-objective optimization problems, Artificial Intelligence, 2015, 228: 45-65.多目标优化问题的双目标演化

8.CA-MOEA

Y. Hua, Y. Jin, K. Hao, A clustering-based adaptive Evolutionary algorithm for multiobjective optimization with irregular Pareto fronts, IEEE Transactions on Cybernetics, 2018.一种基于聚类的多目标优化自适应进化算法

9.CCMO

Y. Tian, T. Zhang, J. Xiao, X. Zhang, and Y. Jin, A coevolutionary framework for constrained multi-objective optimization problems, IEEE Transactions on Evolutionary Computation, 2020.约束多目标优化问题的协同进化框架

10.C-MOEA-D

H. Jain and K. Deb, An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part II: Handling constraints and extending to an adaptive approach, IEEE Transactions on Evolutionary Computation, 2014, 18(4): 602-622.一种基于参考点的非支配排序方法的进化多目标优化算法，第二部分：处理约束并扩展到自适应方法。

11.CMOEA-MS

Y. Tian, Y. Zhang, Y. Su, X. Zhang, K. C. Tan, and Y. Jin, Balancing objective optimization and constraint satisfaction in constrained evolutionary multi-objective optimization, IEEE Transactions on Cybernetics, 2020在约束进化多目标优化中平衡目标优化和约束满足。

12.CMOPSO

X. Zhang, X. Zheng, R. Cheng, J. Qiu, and Y. Jin, A competitive mechanism based multi-objective particle swarm optimizer with fast convergence,Information Sciences, 2018, 427: 63-76.
一种基于竞争机制的快速收敛多目标粒子群优化器

13.CPSMOEA

J. Zhang, A. Zhou, and G. Zhang, A classification and Pareto domination based multiobjective evolutionary algorithm, Proceedings of the IEEE Congress on Evolutionary Computation, 2015, 2883-2890.一种基于分类和帕累托支配的多目标进化算法

14.CSEA

L. Pan, C. He, Y. Tian, H. Wang, X. Zhang, and Y. Jin, A classification based surrogate-assisted evolutionary algorithm for expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, 2018.一种基于分类的代理辅助进化算法，用于昂贵的多目标优化

15.C-TAEA

K. Li, R. Chen, G. Fu, and X. Yao, Two-archive evolutionary algorithm for constrained multi-objective optimization, IEEE Transactions on Evolutionary Computation, 2018, 23(2): 303-315.约束多目标优化的双归档集进化算法

16.DGEA

C. He, R. Cheng, and D. Yazdani, Adaptive offspring generation for evolutionary large-scale multiobjective optimization, IEEE Transactions on System, Man, and Cybernetics: Systems, 2020.进化大规模多目标优化的自适应后代生成

17.DMOEAeC

J. Chen, J. Li, and B. Xin, DMOEA-εC: Decomposition-based multiobjective evolutionary algorithm with the ε-constraint framework, IEEE Transactions on Evolutionary Computation, 2017, 21(5): 714-730.基于分解的多目标进化算法与ε约束框架

18.dMOPSO

S. Z. Martinez and C. A. Coello Coello, A multi-objective particle swarm optimizer based on decomposition, Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 2011, 69-76.

19.DWU

G. Moreira and L. Paquete, Guiding under uniformity measure in the decision space, Proceedings of the 2019 IEEE Latin American Conference on Computational Intelligence, 2019.在决策空间的均匀性度量下进行指导

20.EAGMOEAD

X. Cai, Y. Li, Z. Fan, and Q. Zhang, An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization, IEEE Transactions on Evolutionary Computation, 2015, 19(4): 508-523.一种基于分解的外部归档集引导多目标进化算法

21.EFRRR

Y. Yuan, H. Xu, B. Wang, B. Zhang, and X. Yao, Balancing convergence and diversity in decomposition-based many-objective optimizers, IEEE Transactions on Evolutionary Computation, 2016, 20(2): 180-198. 基于分解的多目标优化器中的平衡收敛性和多样性

22.EIMEGO

D. Zhan, Y. Cheng, and J. Liu, Expected improvement matrix-based infill criteria for expensive multiobjective optimization, IEEE Transactions on Evolutionary Computation, 2017, 21(6): 956-975.

23.eMOEA

K. Deb, M. Mohan, and S. Mishra, Towards a quick computation of well-spread Pareto-optimal solutions, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2003, 222-236.

24.EMyOC

R. Denysiuk, L. Costa, and I. E. Santo, Clustering-based selection for evolutionary many-objective optimization, Proceedings of the International Conference on Parallel Problem Solving from Nature, 2014,538-547.

25.ENSMOEAD

S. Zhao, P. N. Suganthan, and Q. Zhang, Decomposition-based multi- objective evolutionary algorithm with an ensemble of neighborhood sizes, IEEE Transactions on Evolutionary Computation, 2012, 16(3): 442-446.

26.GDE3

S. Kukkonen and J. Lampinen, GDE3: The third evolution step of generalized differential evolution, Proceedings of the IEEE Congress on Evolutionary Computation, 2005, 443-450.

27.GFMMOEA

Y. Tian, X. Zhang, R. Cheng, C. He, and Y. Jin, Guiding evolutionary multi-objective optimization with generic front modeling, IEEE Transactions on Cybernetics, 2018.用通用前端建模指导进化多目标优化

28.GLMO

H. Zille, Large-scale Multi-objective Optimisation: New Approaches and a Classification of the State-of-the-Art, PhD Thesis, Otto von Guericke University Magdeburg, 2019.新的方法和最先进的分类

29.gNSGAII

J. Molina, L. V. Santana, A .G. Hernandez-Diaz, C. A. Coello Coello, and R.Caballero, g-dominance: Reference point based dominance for multiobjective metaheuristics, European Journal of Operational Research,2009, 197(2): 685-692.基于参考点的多目标元启发式优势

30.GrEA

S. Yang, M. Li, X. Liu, and J. Zheng, A grid-based evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2013, 17(5): 721-736.一种基于网格的多目标优化进化算法

31.hpaEA

H. Chen, Y. Tian, W. Pedrycz, G. Wu, R. Wang, and L. Wang, Hyperplane assisted evolutionary algorithm for many-objective optimization problems, IEEE Transactions on Cybernetics, 2019.多目标优化问题的超平面辅助进化算法

32.HypE

J. Bader and E. Zitzler, HypE: An algorithm for fast hypervolume-based many-objective optimization, Evolutionary Computation, 2011, 19(1):45-76.一种基于快速超体积多目标优化的算法

33.IBEA

E. Zitzler and S. Kunzli, Indicator-based selection in multiobjective search, Proceedings of the International Conference on Parallel Problem Solving from Nature, 2004, 832-842.多目标搜索中基于指标的选择

34.IDBEA

M. Asafuddoula, T. Ray, and R. Sarker, A decomposition-based evolutionary algorithm for many objective optimization, IEEE Transactions on Evolutionary Computation, 2015, 19(3): 445-460.一种基于多目标优化的分解的进化算法

35.IMMOEA

R. Cheng, Y. Jin, K. Narukawa, and B. Sendhoff, A multiobjective evolutionary algorithm using Gaussian process-based inverse modeling, IEEE Transactions on Evolutionary Computation, 2015, 19(6): 838-856.

36.ISIBEA

T. Chugh, K. Sindhya, J. Hakanen, and K. Miettinen, An interactive simple indicator-based evolutionary algorithm (I-SIBEA) for multiobjective optimization problems, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2015, 277-291.一种基于交互简单指标的多目标优化问题进化算法

37.KnEA

X. Zhang, Y. Tian, and Y. Jin, A knee point-driven evolutionary algorithm
for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2015, 19(6): 761-776.一种用于多目标优化的膝点驱动进化算法

38.KRVEA

T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya, A surrogate- assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, 2018, 22(1): 129-142.一种代理辅助参考向量引导的进化算法，用于计算昂贵的多目标优化

39.LCSA

H. Zille, Large-scale Multi-objective Optimisation: New Approaches and a Classification of the State-of-the-Art, PhD Thesis, Otto von Guericke University Magdeburg, 2019. 新的方法和最先进的分类

40.LMEA

X. Zhang, Y. Tian, R. Cheng, and Y. Jin, A decision variable clustering based evolutionary algorithm for large-scale many-objective optimization, IEEE Transactions on Evolutionary Computation, 2018, 22(1): 97-112.一种基于决策变量聚类的大规模多目标优化进化算法

41.LMOCSO

Y. Tian, X. Zheng, X. Zhang, and Y. Jin, Efficient large-scale multi-objective optimization based on a competitive swarm optimizer, IEEE Transactions on Cybernetics, 2019.基于竞争群优化器的高效大规模多目标优化

42.LSMOF

C. He, L. Li, Y. Tian, X. Zhang, R. Cheng, Y. Jin, and X. Yao, Accelerating large-scale multi-objective optimization via problem reformulation, IEEE Transactions on Evolutionary Computation, 2019.通过问题重构加速大规模多目标优化

43.MaOEACSS

Z. He and G. G. Yen, Many-objective evolutionary algorithms based on coordinated selection strategy, IEEE Transactions on Evolutionary Computation, 2017, 21(2): 220-233.基于协调选择策略的多目标进化算法

44.MaOEADDFC

J. Cheng, G. G. Yen, and G. Zhang, A many-objective evolutionary algorithm with enhanced mating and environmental selections, IEEE Transactions on Evolutionary Computation, 2015, 19(4): 592-605.一种具有增强交配和环境选择的多目标进化算法

45.MaOEAIGD

Y. Sun, G. G. Yen, and Z. Yi, IGD indicator-based evolutionary algorithm for many-objective optimization problems, IEEE Transactions on Evolutionary Computation, 2018.基于IGD指标的多目标优化问题进化算法

46.MaOEAIT

Y. Sun, B. Xue, M. Zhang, G. G. Yen, A new two-stage evolutionary
algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2018.一种新的多目标优化两阶段进化算法

47.MaOEARD

Z. He and G. G. Yen, Many-objective evolutionary algorithm: Objective space reduction and diversity improvement, IEEE Transactions on Evolutionary Computation, 2016, 20(1): 145-160.客观空间减少和多样性改善

48.MESMO

S. Belakaria, A. Deshwal, J. R. Doppa, Max-value Entropy Search for
Multi-Objective Bayesian Optimization, Proceedings of the 33rd Conference on Neural Information Processing Systems, 2019, 7825-7835.多目标贝叶斯优化的最大值熵搜索

49.MMOPSO

Q. Lin, J. Li, Z. Du, J. Chen, and Z. Ming, A novel multi-objective particle swarm optimization with multiple search strategies, European Journal of Operational Research, 2015, 247(3): 732-744.一种新的具有多种搜索策略的多目标粒子群算法

50.MOCell

A. J. Nebro, J. J. Durillo, F. Luna, B. Dorronsoro, and E. Alba, MOCell: A cellular genetic algorithm for multiobjective optimization, International Journal of Intelligent Systems, 2009, 24(7): 726-746.一种用于多目标优化的细胞遗传算法

51.MOCMA

C. Igel, N. Hansen, and S. Roth, Covariance matrix adaptation for multi- objective optimization, Evolutionary computation, 2007, 15(1): 1-28.协方差矩阵适应多目标优化

52.MOEAD

Q. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 2007,11(6): 712-731.一种基于分解的多目标进化算法

53.MOEADAWA

Y. Qi, X. Ma, F. Liu, L. Jiao, J. Sun, and J. Wu, MOEA/D with adaptive weight adjustment, Evolutionary Computation, 2014, 22(2): 231-264.具有自适应权重调整、进化计算的MOEA/d

54.MOEADCMA

H. Li, Q. Zhang, and J. Deng, Biased multiobjective optimization and decomposition algorithm, IEEE Transactions on Cybernetics, 2017, 47(1): 52-66.

55.MOEADD

K. Li, K. Deb, Q. Zhang, and S. Kwong, An evolutionary many-objective optimization algorithm based on dominance and decomposition, IEEE Transactions Evolutionary Computation, 2015, 19(5): 694-716.一种基于支配和分解的进化多目标优化算法

56.MOEADDAE

K. Li, Q. Zhang, S. Kwong, M. Li, and R. Wang, A constrained multi-objective evolutionary algorithm with detect-and-escape strategy, IEEE Transactions on Evolutionary Computation, 2020.一种具有检测逃逸策略的约束多目标进化算法

57.MOEADDE

H. Li and Q. Zhang, Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Transactions on Evolutionary
Computation, 2009, 13(2): 284-302.复杂帕累托集的多目标优化问题

58.MOEADDRA

Q. Zhang, W. Liu, and H. Li, The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, Proceedings of the IEEE Congress on Evolutionary Computation, 2009, 203-208.

59.MOEADDU

Y. Yuan, H. Xu, B. Wang, B. Zhang, and X. Yao, Balancing convergence and diversity in decomposition-based many-objective optimizers, IEEE
Transactions on Evolutionary Computation, 2016, 20(2): 180-198.基于分解的多目标优化器中的平衡收敛性和多样性

60.MOEADEGO

Q. Zhang, W. Liu, E. Tsang, and B. Virginas, Expensive multiobjective optimization by MOEA/D with Gaussian process model, IEEE Transactions on Evolutionary Computation, 2010, 14(3): 456-474.

61.MOEADFRRMAB

K. Li, A. Fialho, S. Kwong, and Q. Zhang, Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 2014, 18(1): 114-130.基于分解的多目标进化算法的自适应算子选择

62.MOEADM2M

H. Liu, F. Gu, and Q. Zhang, Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems, IEEE Transactions on Evolutionary Computation, 2014, 18(3): 450-455.将多目标优化问题分解为多个简单的多目标子问题

63.MOEADMRDL

S. B. Gee, K. C. Tan, V. A. Shim, and N. R. Pal, Online diversity assessment in evolutionary multiobjective optimization: A geometrical perspective, IEEE Transactions on Evolutionary Computation, 2015, 19(4): 542-559.进化多目标优化中的在线多样性评估

64.MOEADPaS

R. Wang, Q. Zhang, and T. Zhang, Decomposition-based algorithms using Pareto adaptive scalarizing methods, IEEE Transactions on Evolutionary Computation, 2016, 20(6): 821-837.基于分解的算法使用帕累托自适应标量化方法

65.MOEADSTM

K. Li, Q. Zhang, S. Kwong, M. Li, and R. Wang, Stable matching-based selection in evolutionary multiobjective optimization, IEEE Transactions on Evolutionary Computation, 2014, 18(6): 909-923.基于稳定匹配的进化多目标优化选择

66.MOEADURAW

L. R. C. Farias and A. F. R. Araujo, Many-objective evolutionary
algorithm based on decomposition with random and adaptive weights. In Proceedings of the 2019 IEEE International Conference on Systems, Mans and Cybernetics.基于随机和自适应权值分解的多目标进化算法

67.MOEADVA

X. Ma, F. Liu, Y. Qi, X. Wang, L. Li, L. Jiao, M. Yin, and M. Gong, A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables, IEEE Transactions Evolutionary Computation, 2016, 20(2): 275-298.一种基于决策变量分析的大规模变量多目标优化问题的多目标进化算法

68.MOEAIGDNS

Y. Tian, X. Zhang, R. Cheng, and Y. Jin, A multi-objective evolutionary algorithm based on an enhanced inverted generational distance metric,
Proceedings of the IEEE Congress on Evolutionary Computation, 2016,5222-5229.

69.MOEAPC

R. Denysiuk, L. Costa, I. E. Santo, and J. C. Matos, MOEA/PC: Multiobjective evolutionary algorithm based on polar coordinates, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2015, 141-155.基于极坐标的多目标进化算法

70.MOEAPSL

Y. Tian, C. Lu, X. Zhang, K. C. Tan, and Y. Jin, Solving large-scale
multi-objective optimization problems with sparse optimal solutions via unsupervised neural networks, IEEE Transactions on Cybernetics, 2020.用稀疏最优解通过无监督神经网络求解大规模多目标优化问题

71.MOMBIII

R. Hernandez Gomez and C. A. Coello Coello, Improved metaheuristic based on the R2 indicator for many-objective optimization, Proceedings of the Annual Conference on Genetic and Evolutionary Computation, 2015, 679-686.基于R2指标的改进元启发式多目标优化

72.MOPSO

C. A. Coello Coello and M. S. Lechuga, MOPSO: A proposal for multiple objective particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation, 2002, 1051-1056.

73.MOPSOCD

C. R. Raquel and P. C. Naval Jr, An effective use of crowding distance in multiobjective particle swarm optimization, Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, 2005, 257-264.

74.MPAES

J. D. Knowles and D. W. Corne, M-PAES: A memetic algorithm for multiobjective optimization, Proceedings of the IEEE Congress on Evolutionary Computation, 2000, 325-332.

75.MPSOD

C. Dai, Y. Wang, and M. Ye, A new multi-objective particle swarm optimization algorithm based on decomposition, Information Sciences, 2015, 325: 541-557. 一种新的基于分解的多目标粒子群优化算法

76.MSEA

Y. Tian, C. He, R. Cheng, and X. Zhang, A multi-stage evolutionary algorithm for better diversity preservation in multi-objective optimization, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019.一种在多目标优化中更好地保持多样性的多级进化算法

77.MSOPSII

E. J. Hughes, MSOPS-II: A general-purpose many-objective optimiser, Proceedings of the IEEE Congress on Evolutionary Computation, 2007, 3944-3951.

78.MTS

L. Y. Tseng and C. Chen, Multiple trajectory search for unconstrained / constrained multi-objective optimization, Proceedings of the IEEE Congress on Evolutionary Computation, 2009, 1951-1958.多轨迹搜索无约束/约束多目标优化

79.MultiObjectiveEGO

R. Hussein, K. Deb, A Generative Kriging Surrogate Model for Constrained and Unconstrained Multi-objective Optimization, in: Proc. Genet. Evol. Comput. Conf. 2016, Denver, 2016: pp. 573?580.

80.MyODEMR

R. Denysiuk, L. Costa, and I. E. Santo, Many-objective optimization using differential evolution with variable-wise mutation restriction, Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 2013, 591-598.

81.NMPSO

Q. Lin, S. Liu, Q. Zhu, C. Tang, R. Song, J. Chen, C. A. Coello Coello, K. Wong, and J. Zhang, Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems, IEEE Transactions on Evolutionary Computation, 2018, 22(1): 32-46.粒子群优化具有可平衡适应度估计的多目标优化问题

82.NNIA

M. Gong, L. Jiao, H. Du, and L. Bo, Multiobjective immune algorithm with
nondominated neighbor-based selection, Evolutionary Computation, 2008,16(2): 225-255.基于非主导邻居选择的多目标免疫算法

83.NSGAII

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

84.NSGAIIconflict

A. L. Jaimes, C. A. Coello Coello, H. Aguirre, and K. Tanaka, Objective space partitioning using conflict information for solving many-objective problems, Information Sciences, 2014, 268: 305-327.

85.NSGAIII

K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: Solving problems with box constraints, IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.一种基于参考点的非支配排序方法的进化多目标优化算法，第一部分：用盒约束求解问题，

86.NSGAIISDR

Y. Tian, R. Cheng, X. Zhang, Y. Su, and Y. Jin, A strengthened dominance relation considering convergence and diversity for evolutionary many-
objective optimization, IEEE Transactions on Evolutionary Computation,2018.考虑收敛和多样性的强化优势关系用于进化多目标优化

87.NSLS

B. Chen, W. Zeng, Y. Lin, and D. Zhang, A new local search-based multiobjective optimization algorithm, IEEE Transactions on Evolutionary Computation, 2015, 19(1): 50-73.一种新的基于局部搜索的多目标优化算法

88.onebyoneEA

Y. Liu, D. Gong, J. Sun, and Y. Jin, A many-objective evolutionary algorithm using a one-by-one selection strategy, IEEE Transactions on Cybernetics, 2017, 47(9): 2689-2702.一种使用一对一选择策略的多目标进化算法

89.OSP_NSDE

E. Guerrero-Pena, A. F. R. Araujo, Multi-objective evolutionary algorithm with prediction in the objective space, Information Sciences, 2019, 501: 293-316.目标空间预测的多目标进化算法

90.ParEGO

J. Knowles, ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems, IEEE Transactions on Evolutionary Computation, 2006, 10(1): 50-66.

91.PESAII

D. W. Corne, N. R. Jerram, J. D. Knowles, and M. J. Oates, PESA-II: Region-based selection in evolutionary multiobjective optimization, Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, 2001, 283-290.

92.PICEAg

R. Wang, R. C. P.urshouse, and P. J. Fleming, Preference-inspired coevolutionary algorithms for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2013, 17(4): 474-494(多目标优化的偏好激励协同进化算法)

93.PPS

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Push and pull search for solving constrained multi-objective optimization problems, Swarm and Evolutionary Computation, 2019, 44(2): 665-679.推拉搜索求解约束多目标优化问题

94.PREA

J. Yuan, H. Liu, F. Gu, Q. Zhang, and Z. He, Investigating the properties of indicators and an evolutionary many-objective algorithm based on a promising region, IEEE Transactions on Evolutionary Computation, 2020.研究了指标的性质和一种基于有前途区域的进化多目标算法

95.RMMEDA

Q. Zhang, A. Zhou, and Y. Jin, RM-MEDA: A regularity model-based
multiobjective estimation of distribution algorithm, IEEE Transactions on Evolutionary Computation, 2008, 12(1): 41-63.基于规则模型的分布算法多目标估计

96.rNSGAII

L. B. Said, S. Bechikh, and K. Ghedira, The r-dominance: A new dominance
relation for interactive evolutionary multicriteria decision making, IEEE Transactions on Evolutionary Computation, 2010, 14(5): 801-818.

97.RPDNSGAII

M. Elarbi, S. Bechikh, A. Gupta, L. B. Said, and Y. S. Ong, A new decomposition-based NSGA-II for many-objective optimization, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(7): 1191-1210.一种新的基于分解的NSGA-II多目标优化方法

98.RPEA

Y. Liu, D. Gong, X. Sun, and Y. Zhang, Many-objective evolutionary optimization based on reference points, Applied Soft Computing, 2017, 50: 344-355.
基于参考点的多目标进化优化

99.RSEA

C. He, Y. Tian, Y. Jin, X. Zhang, and L. Pan, A radial space division based evolutionary algorithm for many-objective optimization, Applied Soft Computing, 2017, 61: 603-621.一种基于径向空间划分的多目标优化进化算法

100.RVEA

R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, A reference vector guided
evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2016, 20(5): 773-791.一种用于多目标优化的参考向量引导进化算法

101.RVEAa

R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2016, 20(5): 773-791.一种用于多目标优化的参考向量引导进化算法

102.S3CMAES

H. Chen, R. Cheng, J. Wen, H. Li, and J. Weng, Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations, Information Sciences, 2018.用协方差矩阵自适应演化策略求解具有可扩展的小种群的大规模多目标优化问题

103.SCDAS

H. Sato, H. E. Aguirre, and K. Tanaka, Self-controlling dominance area of solutions in evolutionary many-objective optimization, Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning, 2010, 455-465.

104.SIBEA

E. Zitzler, D. Brockhoff, and L. Thiele, The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2007, 862-876.

105.SIBEAkEMOSS

D. Brockhoff and E. Zitzler, Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods, Proceedings of the IEEE Congress on Evolutionary Computation, 2007, 2086-2093.

106.SMEA

H. Zhang, A. Zhou, S. Song, Q. Zhang, X. Gao, and J. Zhang, A self- organizing multiobjective evolutionary algorithm, IEEE Transactions on Evolutionary Computation, 2016, 20(5): 792-806.

107.SMPSO

A. J. Nebro, J. J. Durillo, J. Garcia-Nieto, C. A. Coello Coello, F. Luna, and E. Alba, SMPSO: A new PSO-based metaheuristic for multi-objective optimization, Proceedings of the IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, 2009, 66-73.

108.SMSEGO

W. Ponweiser, T. Wagner, D. Biermann, and M. Vincze, Multiobjective optimization on a limited budget of evaluations using model-assisted S-metric selection, Proceedings of the International Conference on Parallel Problem Solving from Nature, 2008, 784-794.

109.SMSEMOA

M. Emmerich, N. Beume, and B. Naujoks, An EMO algorithm using the hypervolume measure as selection criterion, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2005, 62-76.

110.SparseEA

Y. Tian, X. Zhang, C. Wang, and Y. Jin, An evolutionary algorithm for large-scale sparse multi-objective optimization problems, IEEE Transactions on Evolutionary Computation, 2019.大规模稀疏多目标优化问题的进化算法

111.SPEA2

E. Zitzler, M. Laumanns, and L. Thiele, SPEA2: Improving the strength ，Pareto evolutionary algorithm, Proceedings of the Fifth Conference on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, 2001, 95-100.

112.SPEA2SDE

M. Li, S. Yang, and X. Liu, Shift-based density estimation for Pareto-based algorithms in many-objective optimization, IEEE Transactions on Evolutionary Computation, 2014, 18(3): 348-365.

113.SPEAR

S. Jiang and S. Yang, A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization, IEEE Transactions on Evolutionary Computation, 2017, 21(3): 329-346.一种基于参考方向的多目标和多目标优化的强度帕累托进化算法

114.SRA

B.Li, K.Tang, J. Li, and X. Yao, Stochastic ranking algorithm for many-objective optimization based on multiple indicators, IEEE Transactions on Evolutionary Computation, 2016, 20(6): 924-938.基于多指标的多目标优化随机排序算法

115.tDEA

Y. Yuan, H. Xu, B. Wang, and X. Yao, A new dominance relation-based evolutionary algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2016, 20(1): 16-37.一种新的基于优势关系的多目标优化进化算法

116.TiGE_2

Y. Zhou, Z. Min, J. Wang, Z. Zhang, and J.Zhang, Tri-goal evolution framework for constrained many-objective optimization, IEEE Transactions on Systems Man and Cybernetics Systems, 2018.约束多目标优化的三目标演化框架

117.ToP

Z. Liu and Y. Wang, Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces. IEEE Transactions on Evolutionary Computation, 2019.在决策空间和目标空间中处理约束多目标优化问题

118.Two_Arch2

H. Wang, L. Jiao, and X. Yao, Two_Arch2: An improved two-archive algorithm for many-objective optimization, IEEE Transactions on Evolutionary Computation, 2015, 19(4): 524-541.一种改进的多目标优化双归档集算法

119.VaEA

Y. Xiang, Y. Zhou, M. Li, and Z. Chen, A vector angle-based evolutionary algorithm for unconstrained many-objective optimization, IEEE Transactions on Evolutionary Computation, 2017, 21(1): 131-152.一种基于向量角度的无约束多目标优化进化算法

120.WOF

H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, A framework for large-scale multiobjective optimization based on problem transformation, IEEE Transactions on Evolutionary Computation, 2018, 22(2): 260-275.基于问题变换的大规模多目标优化框架

121.WVMOEAP

X. Zhang, X. Jiang, and L. Zhang, A weight vector based multi-objective optimization algorithm with preference, Acta Electronica Sinica (Chinese), 2016, 44(11): 2639-2645.一种基于权值向量的多目标优化算法

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来源： https://blog.csdn.net/muyouzhutian/article/details/112548846