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

Edmund Burke, Tim Curtois, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Sanja Petrovic, Jose A. Vazquez-Rodriguez, Michel Gendreau
2010 IEEE Congress on Evolutionary Computation  
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
more » ... 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 to two factors: (i) the simple yet powerful exploration/exploitation balance achieved by systematically combining a perturbation followed by local search; and (ii) its parameter-less nature. We believe that the challenge is still open for the design of robust algorithms that can learn and adapt to the available low-level heuristics, and thus select and apply them accordingly.
doi:10.1109/cec.2010.5586064 dblp:conf/cec/BurkeCHKOPRG10 fatcat:p5gne5fuordndhjokcyhzohah4