On the selection of decomposition methods for large scale fully non-separable problems

Yuan Sun, Michael Kirley, Saman K. Halgamuge

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

5 Citations (Scopus)

Abstract

Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. In recent years, a number of decomposition methods have been proposed, which raises another research question: Which decomposition method is best for a given large scale optimization problem? In this paper, we focus on the selection of the best decomposition method for large scale fully non-separable problems. Four decomposition methods are compared on a suite of benchmark functions. We observe that the random grouping method obtains the best solution quality on the benchmark large scale fully non-separable problems.

Original languageEnglish
Title of host publicationProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation Conference
EditorsSara Silva
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1213-1216
Number of pages4
ISBN (Electronic)9781450334884
DOIs
Publication statusPublished - 11 Jul 2015
Externally publishedYes
EventThe Genetic and Evolutionary Computation Conference 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015
Conference number: 17th
http://www.sigevo.org/gecco-2015/
https://dl.acm.org/doi/proceedings/10.1145/2739480 (Proceedings)

Conference

ConferenceThe Genetic and Evolutionary Computation Conference 2015
Abbreviated titleGECCO 2015
Country/TerritorySpain
CityMadrid
Period11/07/1515/07/15
OtherThe Genetic and Evolutionary Computation Conference (GECCO 2015) will present the latest high-quality results in genetic and evolutionary computation. Topics include: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, memetic algorithms, hyper heuristics, real-world applications, evolutionary machine learning, evolvable hardware, artificial life, adaptive behaviour, ant colony optimization, swarm intelligence, biological applications, evolutionary robotics, coevolution, artificial immune systems, and more.
Internet address

Keywords

  • Algorithm selection
  • Cooperative co-evolution
  • Large scale global optimization
  • Problem decomposition

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