求解答。platEMO的问题。

算了一晚上没算出来。
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  1. ZDT：由：Zitzler、Deb和Thiele提出基准的MOP 文献：E. Zitzler, K. Deb, and L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary computation, 2000, 8(2): 173-195. 网址： https://dl.acm.org/doi/10.1162/106365600568202

  2. WFG：由Walking Fish Group提出基准的MOP 文献：S. Huband, P. Hingston, L. Barone, and L. While, A review of multiobjective test problems and a scalable test problem toolkit, IEEE Transactions on Evolutionary Computation, 2006, 10(5): 477-506. 网址： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/profile/Philip_Hingston/publication/3418888_A_review_of_multiobjective_test_problems_and_a_scalable_test_problem_toolkit/links/02e7e5167f703d8e46000000/A-review-of-multiobjective-test-problems-and-a-scalable-test-problem-toolkit.pdf

  3. VNT：由Viennet提出基准的MOP 文献：R. Viennet, C. Fonteix, and I. Marc, Multicriteria optimization using a genetic algorithm for determining a Pareto set, International Journal of Systems Science, 1996, 27(2): 255-260. 网站： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/publication/242928480_Multicriteria_optimization_using_a_genetic_algorithm_for_determining_a_Pareto_set

  4. UF：无约束基准MOP 文献：Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, Multiobjective optimization test instances for the CEC 2009 special session and competition, School of CS & EE, University of Essex, Working Report CES-487, 2009 网站： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/profile/Ponnuthurai_Suganthan/publication/265432807_Multiobjective_optimization_Test_Instances_for_the_CEC_2009_Special_Session_and_Competition/links/54b7d9940cf2c27adc473433.pdf

  5. TREE：时变比误差估计问题 文献：C. He, R. Cheng, C. Zhang, Y. Tian, Q. Chen, and X. Yao, Evolutionary large-scale multiobjective optimization for ratio error estimation of voltage transformers, IEEE Transactions on Evolutionary Computation, 2020. 网站： Evolutionary Large-Scale Multiobjective Optimization for Ratio Error Estimation of Voltage Transformers | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/document/8962275

  1. SMOP：具有稀疏帕累托最优解的MOP 文献：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. 网址： An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/document/8720021

  2. MW：Ma和Wang提出的优约束的MOP 文献：Z. Ma and Y. Wang, Evolutionary constrained multiobjective optimization: Test suite construction and performance comparisons. IEEE Transactions on Evolutionary Computation, 2019. 网址： Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/document/8632683

  3. MOPs in RM-MEDA：基于正则模型多目标分布估计算法的MOP 文献：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. 网址： RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/document/4358761?arnumber=4358761

  4. MOPs in MOEA-D-M2M：基于分解的多目标优化进化算法的MOP问题 文献：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. 网址： Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6595549

  5. MOPs in MOEA-D-DE：基于差分分解的多目标优化算法的MOP问题 文献：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. 网址： Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6595549

  6. MOPs in IM-MOEA：基于高斯过程逆建模的多目标进化算法的MOP问题 文献：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. 网址： A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7018980

  7. MaF：多目标优化的复杂智能系统 文献：R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, A benchmark test suite for evolutionary many-objective optimization, Complex & Intelligent Systems, 2017, 3(1): 67-81. 网址： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/publication/315446832_A_benchmark_test_suite_for_evolutionary_many-objective_optimization

  8. LSMOP：大规模基准的MOP问题 文献：R. Cheng, Y. Jin, and M. Olhofer, Test problems for large-scale multiobjective and many-objective optimization, IEEE Transactions on Cybernetics, 2017, 47(12): 4108-4121. 网址： [PDF] Test Problems for Large-Scale Multiobjective and Many-Objective Optimization | Semantic Scholar The interests in multiobjective and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multiobjective and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multiobjective and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms (EAs) for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multiobjective and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multiobjective and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and nonuniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multiobjective and many-objective EAs are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new EAs dedicated to large-scale multiobjective and many-objective optimization. https://www.semanticscholar.org/paper/Test-Problems-for-Large-Scale-Multiobjective-and-Cheng-Jin/11f96ea3e2a645eddd869579f36615a2e87783c4?p2df

  9. LIRCMOP：具有较大不可行区域的约束基准MOP 文献：Z. Fan, W. Li, X. Cai, H. Huang, Y. Fang, Y. You, J. Mo, C. Wei, and E. Goodman, An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions, Soft Computing, 2019. 网址： 403 Forbidden https://arxiv.org/pdf/1707.08767.pdf

  10. IMOP：不规则Pareto前沿的MOP 文献：Y. Tian, R. Cheng, X. Zhang, M. Li, and Y. Jin, Diversity assessment of multi-objective evolutionary algorithms: Performance metric and benchmark problems, IEEE Computational Intelligence Magazine, 2019. 网址： Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems [Research Frontier] | IEEE Journals & Magazine | IEEE Xplore https://ieeexplore.ieee.org/document/8765427

  11. DTILZ：由Deb, Thiele, Laumanns, and Zitzler提出的MOP问题 文献：K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, Scalable test problems for evolutionary multiobjective optimization, Evolutionary multiobjective Optimization. Theoretical Advances and Applications, 2005, 105-145. 网站： [PDF] Scalable Test Problems for Evolutionary Multiobjective Optimization | Semantic Scholar After adequately demonstrating the ability to solve different two-objective optimization problems, multiobjective evolutionary algorithms (MOEAs) must demonstrate their efficacy in handling problems having more than two objectives. In this study, we have suggested three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of the shape and the location of the resulting Pareto-optimal front, and introduction of controlled difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of the above features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and better understanding of the working principles of MOEAs. https://www.semanticscholar.org/paper/Scalable-Test-Problems-for-Evolutionary-Deb-Thiele/277706e9ea2a0aea2d7433089fee5e163205dc4a?p2df

  12. DOC：决策和目标空间的约束的MOP 文献：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. 网址： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/publication/330580491_Handling_Constrained_Multiobjective_Optimization_Problems_With_Constraints_in_Both_the_Decision_and_Objective_Spaces

  13. MLDMP：多线距离最小化问题 文献：M. Li, C. Grosan, S. Yang, X. Liu, and X. Yao, Multiline distance minimization: A visualized many-objective test problem suite, IEEE Transactions on Evolutionary Computation, 2018, 22(1): 61-78. 网址：https://www.dora.dmu.ac.uk/bitstream/handle/2086/13238/IEEETEVC17-All.pdf?sequence=3&isAllowed=y

  14. DAS-CMOP：困难-可调节和可伸缩的约束基准MOP 文献：Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2019. 网址： [PDF] Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit | Semantic Scholar Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrained many-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving convergence-hard DAS-CMOPs, while mechanisms of NSGA-II-CDP may be more effective in solving DAS-CMOPs with simultaneous diversity-, feasibility-, and convergence-hardness. Mechanisms in C-NSGA-III may be more effective in solving feasibility-hard CMaOPs, while mechanisms of C-MOEA/DD may be more effective in solving CMaOPs with convergence-hardness. In addition, none of them can solve these problems efficiently, which stimulates us to continue to develop new CMOEAs and CMaOEAs to solve the suggested DAS-CMOPs and DAS-CMaOPs. https://www.semanticscholar.org/paper/Difficulty-Adjustable-and-Scalable-Constrained-Test-Fan-Li/5566c8051d467419199b78aa29b1898713e61304?p2df

  15. MOKP：多目标背包问题 文献：E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271. 网站： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/publication/2240388_Thiele_L_Multiobjective_Evolutionary_Algorithms_A_Comparative_Case_Study_and_the_Strength_Pareto_Approach_IEEE_Trans_on_Evolutionary_Computation_3_257-271

    MONRP：多目标Next Release问题 文献：Y. Zhang, M. Harman, and S. A. Mansouri, The multi-objective next release problem, Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, 2007, 1129-1137. 网站：http://www0.cs.ucl.ac.uk/staff/M.Harman/gecco07yz.pdf

MOTSP：多目标旅行商问题 文献：D. Corne and J. Knowles, Techniques for highly multiobjective optimisation: some nondominated points are better than others, Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, 2007, 773-780. 网站： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/profile/Joshua_Knowles/publication/45868377_Techniques_for_Highly_Multiobjective_Optimisation_Some_Nondominated_Points_are_Better_than_Others/links/0912f509274ba30f19000000/Techniques-for-Highly-Multiobjective-Optimisation-Some-Nondominated-Points-are-Better-than-Others.pdf

  mQAP：多目标二次分配问题      文献：J. Knowles and D. Corne, Instance generators and test suites for the multiobjective quadratic assignment problem, Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2003, 295-310.    网站：https://www.mendeley.com/catalogue/d18d5b9a-52b6-3209-9552-b6d48311e066/
  1. CF：约束基准MOP 文献：Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, Multiobjective optimization test instances for the CEC 2009 special session and competition, School of CS & EE, University of Essex, Working Report CES-487, 2009. 网站： ResearchGate ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free. https://www.researchgate.net/profile/Ponnuthurai_Suganthan/publication/265432807_Multiobjective_optimization_Test_Instances_for_the_CEC_2009_Special_Session_and_Competition/links/54b7d9940cf2c27adc473433.pdf
  2. BT：基于偏差特征的MOP 文献：H. Li, Q. Zhang, and J. Deng, Biased multiobjective optimization and decomposition algorithm, IEEE Transactions on Cybernetics, 2017, 47(1): 52-66.
    网站：http://repository.essex.ac.uk/18553/1/07397980.pdf

问题对应一下吧，具体可以打开链接，如果有帮助，请采纳谢谢