Multiobjective selection hyper-heuristics using reinforcement learning - Li, Wenwen (2018)
Considering the multiobjective nature of real-world optimisation problems requiring a search for optimal trade-off solutions, many multiobjective metaheuristics have been proposed in the scientific literature. As observed in previous studies, different approaches show strengths on different problems. A research question would be how to combine the strengths of those multiple multiobjective approaches to obtain improved performance across a range of problems. Hyper-heuristics, which emerged as general-purposed search methods with reusable components, are one of the design philosophies to achieve that goal. Hyper heuristics perform search over the space of (meta)heuristics by either selecting an appropriate (meta)heuristic or generating a new one from given components. Hierarchically, the (meta)heuristics are called low-level (meta)heuristics since they work underneath the selection or generation strategies of the hyper-heuristics. This feature leads to increased generality level for search methods and enables multiobjective hyper-heuristics to be applied to a wide range of problem domains than a metaheuristic tailored for a particular problem domain. A crucial component in such hyper-heuristics is learning and hence, various online and offline learning mechanisms have been adopted within hyper-heuristics.