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 journalArticlepeer-review

Abstract

This paper presents a rough-to-fine evolutionary multiobjective optimization algorithm based on 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 by solving a few of subproblems in the first few levels to obtain a rough Pareto front and gradually refining the Pareto front 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 a lower time complexity. Theoretical analysis shows the complexity of dealing with a new candidate solution is O(Mlog N), where M is the number of objectives. Empirical studies demonstrate the efficacy of the proposed algorithm.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusE-pub ahead of print - 8 Jul 2021

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

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

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