For multi-objective optimization problems, obtaining a uniformly distributed approximation set is among the most important issues. During the past decades, various diversity mechanisms have been proposed to address this challenge. However, the existing diversity mechanisms tend to be problem-specific, and may not generalize well over different problem domains. Inspired by the idea of utilizing multiple low-level heuristics to achieve better diversity performance in multi-discipline problem solving, we focus on efficient algorithm design based on the methodology of selection hyper-heuristics. This study proposes a novel selection hyper-heuristic operating over multiple diversity mechanisms. The unique feature of the proposed approach lies in its ability to intelligently learn, select, and combine different diversity mechanisms with the purpose of taking advantages of them to obtain well-distributed approximation sets. Moreover, this work develops a new learning mechanism, the perturbation adaptive pursuit strategy, which is incorporated into the proposed hyper-heuristic to improve the decision-making process of selecting suitable diversity mechanisms for the problem at hand. The performance of the proposed hyper-heuristic is tested on 2-objective ZDT, 3-objective DTLZ, and 5-objective WFG test suites. Additionally, experiments are also conducted to investigate the ability of the novel hyper-heuristic to integrate existing multi-objective meta-heuristics on MaOP test suite from 3- to 10-objectives. Experimental results demonstrate the effectiveness of the proposed selection hyper-heuristic for cross-domain capacity, particularly in producing well distributed approximation set with respect to Spacing metric and Hypervolume metric.

对于多目标优化问题，获得均匀分布的近似集是最重要的问题。在过去的几十年中，已经提出了各种多样性机制来应对这一挑战。但是，现有的多样性机制倾向于特定于问题，并且可能无法在不同的问题域中很好地概括。受到在多学科问题解决中利用多种低级启发式算法以实现更好的多样性性能的想法的启发，我们专注于基于选择超启发式方法的高效算法设计。这项研究提出了一种新颖的选择超启发式操作多种多样性机制。拟议方法的独特之处在于它能够智能地学习，选择，并结合不同的多样性机制，以利用它们的优势来获得分布良好的近似集。此外，这项工作还开发了一种新的学习机制，即摄动自适应追踪策略，该策略被引入拟议的超启发式算法中，以改善为当前问题选择合适的多样性机制的决策过程。在2目标ZDT，3目标DTLZ和5目标WFG测试套件上测试了所提出的超启发式方法的性能。此外，还进行了实验，以研究新型超启发式方法在3到10个目标的MaOP测试套件上集成现有多目标元启发式方法的能力。实验结果证明了针对跨域容量建议的超启发式选择的有效性，