Multiobjective evolutionary algorithms and reinforcement learning-based hyper-heuristics

This project has two primary foci. First, to gain deep understanding of the pros and cons of different types of multiobjective evolutionary algorithms (MOEAs) and the influences on their performance on both mathematical benchmarks and real-world problems. For example, wind farm layout optimisation and the vehicle crashworthiness problems. The second seeks to design a high-level method to combine the strengths of different MOEAs in an online learning manner. To achieve this goal, a reinforcement learning-based multiobjetive selection hyper-heuristic framework is designed and tested on both benchmark and real-world problems. The experimental results demonstrated the effectiveness and generality of the proposed hyper-heuristic framework.