Hyper-heuristics: A survey and taxonomy

Dokeroglu T., Kucukyilmaz T., Talbi E.

Computers and Industrial Engineering, vol.187, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 187
  • Publication Date: 2024
  • Doi Number: 10.1016/j.cie.2023.109815
  • Journal Name: Computers and Industrial Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Keywords: Hyper-heuristics, Metaheuristics, Optimization, Survey
  • TED University Affiliated: No

Abstract

Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies. Due to the remarkable performance of hyper-heuristics in multi-objective and machine learning-based optimization, there has been an increasing interest in this field. With a fresh perspective, our work extends the current taxonomy and presents an overview of the most significant hyper-heuristic studies of the last two decades. Four categories under which we analyze hyper-heuristics are selection hyper-heuristics (including machine learning techniques), low-level heuristics, target optimization problems, and parallel hyper-heuristics. Future research prospects, trends, and prospective fields of study are also explored.