郑州大学计算智能实验室

Computational Intelligence Laboratory

Code

This column provides two ways to download:

1) Baidu network disk (extraction code is '1234');

2) GitHub;


  • 1) HEL.zip

    K. J. Yu, D. Z. Zhang, J. Liang*, B. Y. Qu, M. N. Liu, K. Chen, C. T. Yue, L. Wang, A Framework Based on Historical Evolution Learning for Dynamic Multiobjective Optimization[J]. IEEE Transactions on Evolutionary Computation, 2023, doi: 10.1109/TEVC.2023.3290485. IF: 16.497, 中科院一区.  

  • 1) MFMOEA.zip

    J. Liang, Y. Y. Zhang, B. Y. Qu, K. Chen, K. J. Yu and C. T. Yue. A Multiform Optimization Framework for Multi-objective Feature Selection in Classification. IEEE Transactions on Evolutionary Computation. 2023. IF: 16.497.

  • 1) CMEGL.zip

    K. J. Qiao, J. Liang*, Z. Y. Liu, K. J. Yu, C. T. Yue, B. Y. Qu. Evolutionary multitasking with global and local auxiliary tasks for constrained multi-objective optimization[J]. IEEE/CAA Journal of Automatica Sinica, 2023. Doi: 10.1109/JAS.2023.123336. IF: 7.847.

  • 1) IMTCMO and SDC benchmark.zip

    K. J. Qiao, J. Liang*, K. J. Yu, C. T. Yue, H. Y. Lin, D. Z. Zhang, B. Y. Qu. Evolutionary constrained multiobjective optimization: scalable high-dimensional constraint benchmarks and algorithm[J]. IEEE Transactions on Evolutionary Computation, 2022. Doi: 10.1109/TEVC.2023.3281666. IF: 16.497.

  • 1) URCMO.zip

    J. J. Liang, K. J. Qiao, K. J. Yu*, B. Y. Qu, C. T. Yue, W. F. Guo, L. Wang. Utilizing the Relationship between Unconstrained and Constrained Pareto Fronts for Constrained Multi-Objective Optimization. IEEE Transactions on Cybernetics, 2022, Doi: 10.1109/TCYB.2022.3163759. IF: 11.448.

  • 1) MTCMO.zip

    K. J. Qiao, K. J. Yu, B. Y. Qu, J. J. Liang*, H. Song, C. T. Yue, H. Y. Lin, K. C. Tan. Dynamic Auxiliary Task-Based Evolutionary Multitasking for Constrained Multi-objective Optimization [J]. IEEE Transactions on Evolutionary Computation, 2022. Doi: 10.1109/TEVC.2022.3175065. IF: 11.554.

  • 1) EMCMO.zip

    2) EMCMO.zip

    K. J. Qiao, K. J. Yu, B. Y. Qu, J. J. Liang*, H. Song, C. T. Yue. An Evolutionary Multitasking Optimization Framework for Constrained Multi-objective Optimization Problems [J]. IEEE Transactions on Evolutionary Computation, 2022. IF: 11.554.

  • 1) MMODE_ICD.zip

  • 2) MMODE_ICD.zip

    C. T. Yue, P. N. Suganthan*, J. J. Liang*, B. Y. Qu, K. J. Yu, Y. S. Zhu, and Y. Li. Differential Evolution Using Improved Crowding Distance for Multimodal Multiobjective Optimization[J]. Swarm and Evolutionary Computation, 2021, 62: 100849. IF: 7.717.

  • 1) code_MMODE_CSCD.zip

    2) code_MMODE_CSCD.zip

    J. J. Liang, K. J. Qiao, C. T. Yue, K. J. Yu, B. Y. Qu*, R. H. Xu, Z. M. Li, and Y. Hu. A Clustering-Based Differential Evolution Algorithm for Solving Multimodal Multi-Objective Optimization Problems[J]. Swarm and Evolutionary Computation, 2020, 60: 100788. IF: 7.717.

  • 1) SEDE.zip

    2) SEDE.zip

    J. J. Liang, K. J. Qiao, K. J. Yu*, S. L. Ge, B. Y. Qu, R. H. Xu, and K. Li. Parameters Estimation of Solar Photovoltaic Models via A Self-Adaptive Ensemble-Base Differential Evolution[J]. Solar Energy, 2020, 207: 336-346. IF: 5.742.

  • 1) SSMOPSO code.zip

    2) SSMOPSO code.zip

    B. Qu, C. Li, J. Liang, L. Yan, K. Yu, Y. Zhu. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems[J]. Applied Soft Computing, 2020, 86: 105886.

  • 1) MMO.zip

    2) MMO.zip

    C. T. Yue, B. Y. Qu, K. J. Yu, J. J. Liang*, and X. D. Li. A Novel Scalable Test Problem Suite for Multimodal Multiobjective Optimization[J]. Swarm and Evolutionary Computation, 2019, 48: 62-71. IF: 6.912.

  • 1) MMOPIO code

    2) MMOPIO code

    Y. Hu, J. Wang, J. J. Liang*, K. J. Yu, H. Song, Q. Q. Guo, C. T. Yue, and Y. L. Wang. A Self-Organizing Multimodal Multiobjective Pigeon-Inspired Optimization Algorithm[J]. Science China-Information Sciences, 2019, 62(7): 70206. IF: 3.304.

  • 1) PGJAYA.zip

    2) PGJAYA.zip

    K. J. Yu, B. Y. Qu, C. T. Yue, S. L. Ge, X. Chen, and J. J. Liang*. A Performance-Guided JAYA Algorithm for Parameters Identification of Photovoltaic Cell and Module[J]. Applied Energy, 2019, 237: 241-257. IF: 8.848.

  • 1) Codes of MMODE.zip

    2) Codes of MMODE.zip

    J. J. Liang, W. W. Xu, C. T. Yue, K. J. Yu, H. Song, O. C. Crisalle, and B. Y. Qu*. Multimodal Multiobjective Optimization with Differential Evolution[J]. Swarm and Evolutionary Computation, 2019, 44: 1028-1059. IF: 6.912.

  • 1) code.zip

    2) code.zip

    Y. S. Zhu, B. H. Qiao, Y. Dong, B. Y. Qu, D. Y. Wu. Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition[J]. IEEJ Transactions on Electrical & Electronic Engineering, 2019, 14(9).1323-1333.

  • 1) MLBSA code.rar

    2) MLBSA code.rar

    K. J. Yu, J. J. Liang*, B. Y. Qu, Z. P. Cheng, and H. S. Wang. Multiple Learning Backtracking Search Algorithm for Estimating Parameters of Photovoltaic Models[J]. Applied Energy, 2018, 226: 408-422. IF: 8.426.

  • 1) MO_Ring_PSO_SCD_codes.zip

    2) MO_Ring_PSO_SCD_codes.zip

    C. T. Yue, B. Y. Qu, and J. J. Liang. A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(5): 805-817. IF: 8.508.

  • 1) IJAYA code.rar

    2) IJAYA code.rar

    K. J. Yu, J. J. Liang*, B. Y. Qu, X. Chen, and H. S. Wang. Parameters Identification of Photovoltaic Models Using an Improved JAYA Optimization Algorithm[J]. Energy Conversion and Management, 2017, 150: 742-753. IF:6.377.

  • 1) 2017-ASOC-EPSO.zip

    2) 2017-ASOC-EPSO.zip

    Lynn, N., & Suganthan, P. N. (2017). Ensemble particle swarm optimizer. Applied Soft Computing, 55, 533-548.

  • 1) 2015-SWEVO-HCLPSO.zip

    2) 2015-SWEVO-HCLPSO.zip

    Lynn, N., & Suganthan, P. N. (2015). Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 24, 11-24.

  • 1) 2013-TEC-LIPS.rar

    2) 2013-TEC-LIPS.rar

    B. Y. Qu, P. N. Suganthan, S. Das. A distance-based locally informed particle swarm model for multimodal optimization[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(3): 387-402.

  • 1) 2012-TEC-DE-niching.zip

    2) 2012-TEC-DE-niching.zip

    B. Y. Qu, P. N. Suganthan, and J. J. Liang. Differential Evolution with Neighborhood Mutation for Multimodal Optimization[J]. IEEE Transactions on Evolutionary Computation, 2012, 16(5): 601-614. IF:4.810.

  • 1) 2012-INS-Niching.rar

    2) 2012-INS-Niching.rar

    B. Y. Qu, J. J. Liang, P. N. Suganthan. Niching Particle Swarm Optimization with Local Search for Multi-modal Optimization[J]. Information Sciences, 2012, 197: 131-143. IF:3.643.

  • 1) 2011-Eng-Opt-2LB-MOPSO.zip

    2) 2011-Eng-Opt-2LB-MOPSO.zip

    Zhao, S. Z., & Suganthan, P. N. (2011). Two-lbests based multi-objective particle swarm optimizer. Engineering Optimization, 43(1), 1-17.

  • 1) 2011-Eng-Opt-Ens-Const-MODE

    2) 2011-Eng-Opt-Ens-Const-MODE

    B. Y. Qu*, P. N. Suganthan*, "Constrained Multi-Objective Optimization Algorithm with Ensemble of Constraint Handling Methods", Engineering Optimization, vol. 43,no.4,pp.403-416, Mar 2011.

  • 1) 2010-Inf-Sci-Fast-Sort-MODE.rar

    2) 2010-Inf-Sci-Fast-Sort-MODE.rar

    B. Y. Qu*, P. N. Suganthan, “Multi-Objective Evolutionary Algorithms based on the Summation of Normalized Objectives and Diversified Selection”, Information Sciences, vol. 180, no. 17, pp. 3170-3181, Sept. 2010.

  • 1) 2006-IEEE-TEC-CLPSO.rar

    2) 2006-IEEE-TEC-CLPSO.rar

    J. J. Liang, P. N. Suganthan, A. K. Qin, S. Baska. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295. IF:11.554.