Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

Borhan KAZIMIPOUR, Mohammad Nabi OMIDVAR, A.K. QIN, Xiaodong LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

18 Citations (Scopus)

Abstract

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms. © 2018 Elsevier B.V.
Original languageEnglish
Pages (from-to)265-281
Number of pages17
JournalApplied Soft Computing Journal
Volume76
Early online date18 Dec 2018
DOIs
Publication statusPublished - Mar 2019
Externally publishedYes

Bibliographical note

This work was supported by National Key R&D Program of China (Grant No. 2017YFC0804003 ), EPSRC (Grant Nos. EP/J017515/1 and EP/P005578/1 ), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386 ), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531 ), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284 ) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008 ).

Keywords

  • Imbalanced large-scale optimization
  • Multi-armed bandits
  • Resource allocation

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