Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms

An important challenge within hyper-heuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across different problem domains. This article conducts an empirical study involving three different domains in combinatorial optimisation: bin packing, permutation flow shop and personnel scheduling. Using a common software interface (HyFlex), the same algorithms (high-level strategies or hyperheuristics) can be readily run on all of them. The study is intended as a proof of concept of the proposed interface and domain modules, as a benchmark for testing the generalisation abilities of heuristic search algorithms. Several algorithms and variants from the literature were implemented and tested. From them, the implementation of iterated local search produced the best overall performance. Interestingly, this is one of the most conceptually simple competing algorithms, its advantage as a robust algorithm is probably due ...

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Edmund K. Burke, Timothy Curtois, Matthew R. Hyde, Graham Kendall, Gabriela Ochoa, Sanja Petrovic, José Antonio Vázquez Rodríguez, Michel Gendreau
• Keywords: Algorithms, Artificial Intelligence, CEC 2010, Heuristic Search Algorithms, Robust Algorithms