A memetic cooperative co-evolution model for large scale continuous optimization

Yuan Sun, Michael Kirley, Saman K. Halgamuge

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1 Citation (Scopus)

Abstract

Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorithm.

Original languageEnglish
Title of host publicationArtificial Life and Computational Intelligence
Subtitle of host publicationThird Australasian Conference, ACALCI 2017, Proceedings
EditorsMarkus Wagner, Xiaodong Li, Tim Hendtlass
Place of PublicationCham Switzerland
PublisherSpringer-Verlag London Ltd.
Pages291-300
Number of pages10
Volume10142
ISBN (Electronic)9783319516912
ISBN (Print)9783319516905
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventAustralasian Conference on Artificial Life and Computational Intelligence 2017 - Deakin University, Geelong, Australia
Duration: 31 Jan 20172 Feb 2017
Conference number: 3rd
http://www.acalci.net/2017/
https://link.springer.com/book/10.1007/978-3-319-51691-2 (Springer Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume10142
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAustralasian Conference on Artificial Life and Computational Intelligence 2017
Abbreviated titleACALCI 2017
CountryAustralia
CityGeelong
Period31/01/172/02/17
OtherACACLI 2017 is co-located with the Australasian Computer Science Week (ACSW 2017), which will be held at Deakin University's Waterfront Campus, Geelong, which is about 70 kilometers west of Mebourne.

3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017
Internet address

Keywords

  • Continuous optimization problem
  • Cooperative co-evolution
  • Large scale global optimization
  • Memetic algorithm

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