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, 687-694.一種快速近似引導的進化多目標算法

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, 2019.一種基于非歐幾里德幾何的多目標優化自適應進化算法

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): 602-622.一種基于參考點的非支配排序方法的進化多目標優化算法，第二部分：處理約束并擴展到自適應方法

4.ARMOEA

An indicator-based multiobjective evolutionary algorithm with reference point adaptation for better versatility. IEEE Transactions on Evolutionary Computation, 2018, 22(4): 609-622.一種基于指標并且具有參考點好的通用性的多目標進化算法。

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：多目標優化中的雙準則演化

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：多目標優化中的雙準則演化

7.BiGE

M. Li, S. Yang, and X. Liu, Bi-goal evolution for many-objective optimization problems, Artificial Intelligence, 2015, 228: 45-65.多目標優化問題的雙目標演化

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, 2018.一種基于聚類的多目標優化自適應進化算法

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.約束多目標優化問題的協同進化框架

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): 602-622.一種基于參考點的非支配排序方法的進化多目標優化算法，第二部分：處理約束并擴展到自適應方法。

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在約束進化多目標優化中平衡目標優化和約束滿足。

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.
一種基于競爭機制的快速收斂多目標粒子群優化器

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, 2883-2890.一種基于分類和帕累托支配的多目標進化算法

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, 2018.一種基于分類的代理輔助進化算法，用于昂貴的多目標優化

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.約束多目標優化的雙歸檔集進化算法

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.進化大規模多目標優化的自適應后代生成

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.基于分解的多目標進化算法與ε約束框架

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.在決策空間的均勻性度量下進行指導

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): 508-523.一種基于分解的外部歸檔集引導多目標進化算法

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. 基于分解的多目標優化器中的平衡收斂性和多樣性

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.基于聚類選擇的多目標優化算法

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.基于分解的具有鄰域大小集合的多目標進化算法

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.用通用前沿建模指導進化多目標優化

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.新的方法和最先進的分類

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.基于參考點的多目標元啟發式支配

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): 721-736.一種基于網格的多目標優化進化算法

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.多目標優化問題的超平面輔助進化算法

32.HypE

J. Bader and E. Zitzler, HypE: An algorithm for fast hypervolume-based many-objective optimization, Evolutionary Computation, 2011, 19(1):45-76.一種基于快速超體積多目標優化的算法

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.多目標搜索中基于指標的選擇

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): 445-460.一種基于多目標優化的分解的進化算法

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): 838-856.一種基于高斯過程的逆建模多目標優化算法

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, 277-291.一種基于交互簡單指標的多目標優化問題進化算法

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): 761-776.一種用于多目標優化的膝點驅動進化算法

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): 129-142.一種用于計算昂貴的多目標優化代理輔助參考向量引導的進化算法，

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. 新的方法和最先進的分類

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): 97-112.一種基于決策變量聚類的大規模多目標優化進化算法

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.基于競爭群優化器的高效大規模多目標優化

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.通過問題重構加速大規模多目標優化

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.基于協調選擇策略的多目標進化算法

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): 592-605.一種具有增強交配和環境選擇的多目標進化算法

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指標的多目標優化問題進化算法

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, 2018.一種新的多目標優化兩階段進化算法

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.目標空間減少和多樣性改善

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.多目標貝葉斯優化的最大值熵搜索

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): 732-744.一種新的具有多種搜索策略的多目標粒子群算法

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): 726-746.一種用于多目標優化的細胞遺傳算法

51.MOCMA

C. Igel, N. Hansen, and S. Roth, Covariance matrix adaptation for multi- objective optimization, Evolutionary computation, 2007, 15(1): 1-28.協方差矩陣適應多目標優化

52.MOEAD

Q. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 2007,11(6): 712-731.一種基于分解的多目標進化算法

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.具有自適應權重調整、進化計算的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): 694-716.一種基于支配和分解的進化多目標優化算法

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, 2020.一種具有檢測逃逸策略的約束多目標進化算法

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.復雜帕累托集的多目標優化問題

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.基于分解的多目標優化器中的平衡收斂性和多樣性

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.基于分解的多目標進化算法的自適應算子選擇

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.將多目標優化問題分解為多個簡單的多目標子問題

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.進化多目標優化中的在線多樣性評估

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.

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.基于穩定匹配的進化多目標優化選擇

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.基于隨機和自適應權值分解的多目標進化算法

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): 275-298.一種基于決策變量分析的大規模變量多目標優化問題的多目標進化算法

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, 2016,5222-5229.一種基于增強反世代距離度量的多目標進化算法

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.基于極坐標的多目標進化算法

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.用稀疏最優解通過無監督神經網絡求解大規模多目標優化問題

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指標的改進元啟發式多目標優化

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. 一種新的基于分解的多目標粒子群優化算法

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, 2019.一種在多目標優化中更好地保持多樣性的多級進化算法

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.多軌跡搜索無約束/約束多目標優化

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.

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.基于非主導鄰域選擇的多目標免疫算法

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): 577-601.一種基于參考點的非支配排序方法的進化多目標優化算法，第一部分：用盒約束求解問題

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.考慮收斂和多樣性的強化支配關系用于進化多目標優化

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): 50-73.一種新的基于局部搜索的多目標優化算法

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): 2689-2702.一種使用一對一選擇策略的多目標進化算法

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.目標空間預測的多目標進化算法

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多目標優化的偏好激勵協同進化算法

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.推拉搜索求解約束多目標優化問題

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.研究了指標的性質和一種基于有前途區域的進化多目標算法

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.基于規則模型的分布算法多目標估計

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): 1191-1210.一種新的基于分解的NSGA-II多目標優化方法

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.基于參考點的多目標進化優化

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: 603-621.一種基于徑向空間劃分的多目標優化進化算法

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): 773-791.一種用于多目標優化的參考向量引導進化算法

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): 773-791.一種用于多目標優化的參考向量引導進化算法

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.用協方差矩陣自適應演化策略求解具有可擴展的小種群的大規模多目標優化問題

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.大規模稀疏多目標優化問題的進化算法

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): 329-346.一種基于參考方向的多目標和多目標優化的強度帕累托進化算法

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.基于多指標的多目標優化隨機排序算法

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): 16-37.一種新的基于支配關系的多目標優化進化算法

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.約束多目標優化的三目標演化框架

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.在決策空間和目標空間中處理約束多目標優化問題

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): 524-541.一種改進的多目標優化雙歸檔集算法

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): 131-152.一種基于向量角度的無約束多目標優化進化算法

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.基于問題變換的大規模多目標優化框架

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): 2639-2645.一種基于權值向量的多目標優化算法

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