Selection hyper-heuristics in dynamic environments

Kiraz B., ETANER-UYAR A. S., Ozcan E.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, vol.64, no.12, pp.1753-1769, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 64 Issue: 12
  • Publication Date: 2013
  • Doi Number: 10.1057/jors.2013.24
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.1753-1769
  • Istanbul Technical University Affiliated: No


Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real-world problems are dynamic in nature, where different types of changes may occur over time. Population-based approaches, such as evolutionary algorithms, are frequently used for solving dynamic environment problems. Selection hyper-heuristics are highly adaptive search methodologies that aim to raise the level of generality by providing solutions to a diverse set of problems having different characteristics. In this study, the performances of 35 single-point-search-based selection hyper-heuristics are investigated on continuous dynamic environments exhibiting various change dynamics, produced by the Moving Peaks Benchmark generator. Even though there are many successful applications of selection hyper-heuristics to discrete optimization problems, to the best of our knowledge, this study is one of the initial applications of selection hyper-heuristics to real-valued optimization as well as being among the very few which address dynamic optimization issues using these techniques. The empirical results indicate that learning selection hyper-heuristics incorporating compatible components can react to different types of changes in the environment and are capable of tracking them. This study shows the suitability of selection hyper-heuristics as solvers in dynamic environments.