A Reinforcement Learning Hyper-Heuristic in Multi-Objective Single Point Search with Application to Structural Fault Identification
This work addresses the need for general, adaptive optimization techniques in engineering design and manufacturing, though it appears incremental as it builds on existing algorithms with a novel hyper-heuristic approach.
The authors tackled the problem of multi-objective optimization in engineering by developing a reinforcement learning hyper-heuristic scheme that adaptively selects heuristics for a single point search algorithm, resulting in improved and more robust performance compared to existing methods like AMOSA, NSGA-II, and MOEA/D on benchmark tests and a structural fault identification problem.
Multi-objective optimizations are frequently encountered in engineering practices. The solution techniques and parametric selections however are usually problem-specific. In this study we formulate a reinforcement learning hyper-heuristic scheme, and propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed) towards multi-objective optimization problems of general applications. Making use of the domination amount, crowding distance and hypervolume calculations, the proposed hyper-heuristic scheme can meet various optimization requirements adaptively and autonomously. The approach developed not only exhibits improved and more robust performance compared to AMOSA, NSGA-II and MOEA/D when applied to benchmark test cases, but also shows promising results when applied to a generic structural fault identification problem. The outcome of this research can be extended to a variety of design and manufacturing optimization applications.