Large-scale evolutionary optimization: A review and comparative study

Jing Liu*, Ruhul Sarker, Saber Elsayed, Daryl Essam, Nurhadi Siswanto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Large-scale global optimization (LSGO) problems have widely appeared in various real-world applications. However, their inherent complexity, coupled with the curse of dimensionality, makes them challenging to solve. Continuous efforts have been devoted to designing computational intelligence-based approaches to solve them. This paper offers a comprehensive review of the latest developments in the field, focusing on the advances in both single-objective and multi-objective large-scale evolutionary optimization algorithms over the past five years. We systematically categorize these algorithms, discuss their distinct features, and highlight benchmark test suites essential for performance evaluation. After that, comparative studies are conducted using numerical solutions to evaluate the performance of state-of-the-art LSGO for both single-objective and multi-objective problems. Finally, we discuss the real-world applications of LSGO, some challenges, and possible future research directions.

Original languageEnglish
Article number101466
JournalSwarm and Evolutionary Computation
Volume85
DOIs
Publication statusPublished - Mar 2024

Keywords

  • Evolutionary optimization
  • High-dimensional problems
  • Large-scale optimization
  • Multi-objective optimization
  • Sparse optimization

Fingerprint

Dive into the research topics of 'Large-scale evolutionary optimization: A review and comparative study'. Together they form a unique fingerprint.

Cite this