Dynamic multi-objective optimization algorithm based decomposition and preference

Yaru Hu, Jinhua Zheng*, Juan Zou, Shouyong Jiang, Shengxiang Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for the approximation of Pareto-optimal front (POF) with well-distributed in handling dynamic multi-objective optimization problems (DMOPs). Nevertheless, in real-world scenarios, the decision maker (DM) may be only interested in a portion of the corresponding POF (i.e., the region of interest) for different instances, rather than the whole POF. Consequently, a novel DMOEA based decomposition and preference (DACP) is proposed, which incorporates the preference of DM into the dynamic search process and tracks a subset of Pareto-optimal set (POS) approximation with respect to the region of interest (ROI). Due to the presence of dynamics, the ROI, which is defined in which DM gives both the preference point and the neighborhood size, may be changing with time-varying DMOPs. Consequently, our algorithm moves the well-distributed reference points, which are located in the neighborhood range, to around the preference point to lead the evolution of the whole population. When a change occurs, a novel strategy is performed for responding to the current change. Particularly, the population will be reinitialized according to a promising direction obtained by letting a few solutions evolve independently for a short time. Comprehensive experiments show that this approach is very competitivecompared with state-of-the-art methods.

Original languageEnglish
Pages (from-to)175-190
Number of pages16
JournalInformation Sciences
Early online date20 Apr 2021
Publication statusPublished - 1 Sept 2021

Bibliographical note

Funding Information:
This work was supported by the research projects: the National Natural Science Foundation of China under Grant Nos. 61772178, 61876164, Xiangtan university graduate research and innovation project under Grant No. XDCX2019B057, The MOEA KeyLaboratory of Intelligent Computing and Information Processing, the Scienceand Technology Plan Project of Hunan Province (Grant No. 2016TP1020),the Provinces and Cities Joint Foundation Project (Grant No. 2017JJ4001),Science and Technology Planning Project of Guangdong Province of China(Grant No. 2017B010111005), the Hunan province science and technologyproject funds (Grant No. 2018TP1036)..


  • Changing preference point
  • Dynamic multi-objective evolutionary algorithms (DMOEAs)
  • Reference points
  • The region of interest (ROI)


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