Hyper-heuristic local search for combinatorial optimisation problems

摘要

Local search algorithms have been successfully used for many combinatorial optimisation problems. The choice of the most suitable local search algorithm is, however, a challenging task as their performance is highly dependent on the problem characteristic. In addition, most of these algorithms require users to select appropriate internal neighbourhood structures to obtain desirable performance. No single local search algorithm can consistently perform well with a fixed setting, for different types of problems or even different instances of the same problem. To address this issue, we propose a hyper-heuristic framework which incorporates multiple local search algorithms and a pool of neighbourhood structures. This framework is novel in three respects. Firstly, a two-stage hyper-heuristic structure is designed to control the selection of a local search algorithm and its internal operators. Secondly, we propose an adaptive ranking mechanism to choose the most appropriate neighbourhood structures for the current local search algorithm. The proposed mechanism uses the entropy to evaluates the contribution of the local search in terms of quality and diversity. It adaptively adjusts the pool of candidate neighbourhood structures. Thirdly, we use a population of solutions within the proposed framework to effectively navigate different areas in the solutions search space and share solutions with local search algorithms. To ensure different solutions is allocated in different regions of the search space, we propose a distance-based strategy for population updating process that allowing solutions to share local search algorithms. We have evaluated the performance of the proposed framework using two challenging optimisation problems: Multi-Capacity Bin Packing benchmark instances and Google Machine Reassignment benchmark instances. The results show the effectiveness of the proposed framework, which outperformed state-of-the-art algorithms on several problem instances.