An ant-based selection hyper-heuristic for dynamic environments

Kiraz, Berna; Etaner-Uyar, A. Şima; Özcan, Ender

Abstract

Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyper-heuristic manages a set of low level heuristics (operators) and decides which one to apply at each iterative step. Recent studies show that selection hyper-heuristic methodologies are indeed suitable for solving dynamic environment problems with their ability of tracking the change dynamics in a given environment. The choice function based selection hyper-heuristic is reported to be the best hyper-heuristic on a set of benchmark problems. In this study, we investigate the performance of a new learning hyper-heuristic and its variants which are inspired from the ant colony optimization algorithm components. The proposed hyper-heuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low level heuristics. The ant-based hyper-heuristic performs better than the choice function and even its improved version across a variety of dynamic environments produced by the Moving Peaks Benchmark generator.

Citation

Kiraz, B., Etaner-Uyar, A. Ş., & Özcan, E. (2013, April). An ant-based selection hyper-heuristic for dynamic environments. Presented at 16th European Conference on Applications of Evolutionary Computing, Vienna, Austria

• Presentation Conference Type: Edited Proceedings
• Conference Name: 16th European Conference on Applications of Evolutionary Computing
• Start Date: Apr 3, 2013
• End Date: Apr 5, 2013
• Publication Date: 2013
• Deposit Date: Jul 26, 2023
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Pages: 626-635
• Series Title: Lecture Notes in Computer Science
• Series Number: 7835
• Series ISSN: 1611-3349
• Book Title: Applications of Evolutionary Computation. EvoApplications 2013.
• ISBN: 9783642371912
• DOI: https://doi.org/10.1007/978-3-642-37192-9_63
• Public URL: https://nottingham-repository.worktribe.com/output/12901497
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-642-37192-9_63