A sequential algorithm portfolio approach for black box optimization

Yaodong HE*, Shiu Yin YUEN, Yang LOU, Xin ZHANG

*Corresponding author for this work

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

7 Citations (Scopus)


A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.

Original languageEnglish
Pages (from-to)559-570
Number of pages12
JournalSwarm and Evolutionary Computation
Early online date1 Aug 2018
Publication statusPublished - Feb 2019
Externally publishedYes

Bibliographical note

Funding Information:
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 125313 ]. Yaodong He acknowledges the Institutional Postgraduate Studentship from City University of Hong Kong .

Publisher Copyright:
© 2018 Elsevier B.V.


  • Algorithm portfolio
  • Algorithm selection
  • Heuristic algorithms
  • Optimization problems
  • Performance prediction


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