Decomposition for Large-scale Optimization Problems with Overlapping Components

Yuan Sun, Xiaodong Li, Andreas Ernst, Mohammad Nabi Omidvar

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)


In this paper we use a divide-and-conquer approach to tackle large-scale optimization problems with overlapping components. Decomposition for an overlapping problem is challenging as its components depend on one another. The existing decomposition methods typically assign all the linked decision variables into one group, thus cannot reduce the original problem size. To address this issue we modify the Recursive Differential Grouping (RDG) method to decompose overlapping problems, by breaking the linkage at variables shared by multiple components. To evaluate the efficacy of our method, we extend two existing overlapping benchmark problems considering various level of overlap. Experimental results show that our method can greatly improve the search ability of an optimization algorithm via divide-and-conquer, and outperforms RDG, random decomposition as well as other state-of-the-art methods. We further evaluate our method using the CEC'2013 benchmark problems and show that our method is very competitive when equipped with a component optimizer.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019
EditorsMengjie Zhang, Kay Chen Tan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728121536, 9781728121529
ISBN (Print)9781728121543
Publication statusPublished - 1 Jun 2019
EventIEEE Congress on Evolutionary Computation 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019 (Proceedings)


ConferenceIEEE Congress on Evolutionary Computation 2019
Abbreviated titleIEEE CEC 2019
CountryNew Zealand
Internet address


  • Cooperative co-evolution
  • large-scale continuous optimization
  • overlapping problem
  • problem decomposition.
  • variable interaction

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