A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization - Part II

Mohammad Nabi OMIDVAR, Xiaodong LI, Xin YAO

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

24 Citations (Scopus)

Abstract

This article is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely, decomposition methods and hybridization methods, such as memetic algorithms and local search. In this part, we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multiobjective optimization, constraint handling, overlapping components, the component imbalance issue and benchmarks, and applications. The article also includes a discussion on pitfalls and challenges of the current research and identifies several potential areas of future research. © 1997-2012 IEEE.
Original languageEnglish
Pages (from-to)823-843
Number of pages21
JournalIEEE Transactions on Evolutionary Computation
Volume26
Issue number5
Early online date25 Nov 2021
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Keywords

  • Black-box optimization
  • evolutionary optimization
  • large-scale global optimization
  • metaheuristics

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