A Fuzzy Decomposition-Based Multi/Many-Objective Evolutionary Algorithm

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)3495-3509
Number of pages15
Journal / PublicationIEEE Transactions on Cybernetics
Issue number5
Online published4 Aug 2020
Publication statusPublished - May 2022



Performance of multi/many-objective evolutionary algorithms (MOEAs) based on decomposition is highly impacted by the Pareto front (PF) shapes of multi/many-objective optimization problems (MOPs), as their adopted weight vectors may not properly fit the PF shapes. To avoid this mismatch, some MOEAs treat solutions as weight vectors to guide the evolutionary search, which can adapt to the target MOP's PF automatically. However, their performance is still affected by the similarity metric used to select weight vectors. To address this issue, this article proposes a fuzzy decomposition-based MOEA. First, a fuzzy prediction is designed to estimate the population's shape, which helps to exactly reflect the similarities of solutions. Then, N least similar solutions are extracted as weight vectors to obtain N constrained fuzzy subproblems (N is the population size), and accordingly, a shared weight vector is calculated for all subproblems to provide a stable search direction. Finally, the corner solution for each of m least similar subproblems (m is the objective number) is preserved to maintain diversity, while one solution having the best aggregated value on the shared weight vector is selected for each of the remaining subproblems to speed up convergence. When compared to several competitive MOEAs in solving a variety of test MOPs, the proposed algorithm shows some advantages at fitting their different PF shapes.

Research Area(s)

  • evolutionary algorithm, multiobjective optimization

Citation Format(s)

A Fuzzy Decomposition-Based Multi/Many-Objective Evolutionary Algorithm. / Liu, Songbai; Lin, Qiuzhen; Tan, Kay Chen et al.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 5, 05.2022, p. 3495-3509.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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