1. A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization
    Junwei Ou, Jinhua Zheng, Gan Ruan, Yaru Hu, Juan Zou, Miqing Li, Shengxiang Yang, Xu Tan. Applied Soft Computing: 2019 ,105673
  2. The effect of diversity maintenance on prediction in dynamic multi-objective optimization
    Ruan, Gan and Yu, Guo and Zheng, Jinhua and Zou, Juan and Yang, Shengxiang. Applied Soft Computing: 2017 ,58 ,631--647 Download
  3. An Adaptation Reference-point-based Multiobjective Evolutionary Algorithm
    Zou, Juan and Fu, Liuwei and Yang, Shengxiang and Zheng, Jinhua and Ruan, Gan and Pei, Tingrui and Wang, Lei. Information Sciences: 2019
  4. A predictive strategy based on special points for evolutionary dynamic multi-objective optimization
    Li, Qingya and Zou, Juan and Yang, Shengxiang and Zheng, Jinhua and Ruan, Gan. Soft Computing: 2018 ,1--17
  5. A proportion-based selection scheme for multi-objective optimization
    Fu, Liuwei and Zou, Juan and Yang, Shengxiang and Ruan, Gan and Ma, Zhongwei and Zheng, Jinhua. 2017 IEEE Symposium Series on Computational Intelligence (SSCI): 2017 ,1--7
  6. An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance
    Zhou, Jianwei and Zou, Juan and Yang, Shengxiang and Ruan, Gan and Ou, Junwei and Zheng, Jinhua. 2018 IEEE Symposium Series on Computational Intelligence (SSCI): 2018 ,2148--2154
  7. A performance indicator for reference-point-based multiobjective evolutionary optimization
    Hou, Zhanglu and Yang, Shengxiang and Zou, Juan and Zheng, Jinhua and Yu, Guo and Ruan, Gan. 2018 IEEE Symposium Series on Computational Intelligence (SSCI): 2018 ,1571--1578
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