A Rough-to-Fine Evolutionary Multiobjective Optimization Algorithm

Fangqing Gu, Hai-Lin Liu*, Yiu-ming Cheung*, Minyi Zheng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


This article presents a rough-to-fine evolutionary multiobjective optimization algorithm based on the decomposition for solving problems in which the solutions are initially far from the Pareto-optimal set. Subsequently, a tree is constructed by a modified k -means algorithm on N uniform weight vectors, and each node of the tree contains a weight vector. Each node is associated with a subproblem with the help of its weight vector. Consequently, a subproblem tree can be established. It is easy to find that the descendant subproblems are refinements of their ancestor subproblems. The proposed algorithm approaches the Pareto front (PF) by solving a few subproblems in the first few levels to obtain a rough PF and gradually refining the PF by involving the subproblems level-by-level. This strategy is highly favorable for solving problems in which the solutions are initially far from the Pareto set. Moreover, the proposed algorithm has lower time complexity. Theoretical analysis shows the complexity of dealing with a new candidate solution is O(M logN) , where M is the number of objectives. Empirical studies demonstrate the efficacy of the proposed algorithm.

Original languageEnglish
Pages (from-to)13472-13485
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number12
Early online date8 Jul 2021
Publication statusPublished - Dec 2022

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Decomposition
  • evolutionary algorithm
  • incremental
  • multiobjective optimization
  • tree-like weight design


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