A Clustering-based Evolutionary Algorithm for Many-objective Optimization Problems

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

87 Scopus Citations
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Author(s)

  • Qiuzhen Lin
  • Maoguo Gong
  • Carlos A. Coello Coello
  • Jianyong Chen
  • Jun Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)391-405
Journal / PublicationIEEE Transactions on Evolutionary Computation
Volume23
Issue number3
Online published23 Aug 2018
Publication statusPublished - Jun 2019

Abstract

This paper suggests a novel clustering-based evolutionary algorithm for many-objective optimization problems. Its main idea is to classify the population into a number of clusters, which is expected to solve the difficulty of balancing convergence and diversity in high dimensional objective space. The individuals showing high similarities on the vector angles are gathered into the same cluster, such that the population’s distribution can be well portrayed by the clusters. To efficiently find these clusters, partitional clustering is first used to classify the union population into m main clusters based on the m axis vectors (m is the number of objectives), and then hierarchical clustering is further run on these m main clusters to get N final clusters (N is the population size and N > m ). At last, in environmental selection, one individual from each of N clusters closest to the axis vectors is selected to maintain diversity, while one individual from each of the other clusters is preferred by a simple convergence indicator to ensure convergence. When tackling some well-known test problems with 5 to 15 objectives, extensive experiments validate the superiority of our algorithm over six competitive many-objective evolutionary algorithms, especially on problems with incomplete and irregular Pareto-optimal fronts.

Research Area(s)

  • Clustering algorithms, Clustering methods, Convergence, Evolutionary algorithm (EA), Evolutionary computation, Hierarchical clustering, Many-objective optimization, Partitional clustering, Phase change materials, Sociology, Statistics

Citation Format(s)

A Clustering-based Evolutionary Algorithm for Many-objective Optimization Problems. / Lin, Qiuzhen; Liu, Songbai; Wong, Ka-Chun et al.
In: IEEE Transactions on Evolutionary Computation, Vol. 23, No. 3, 06.2019, p. 391-405.

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