Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (14): 1-8.DOI: 10.3778/j.issn.1002-8331.1611-0131

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Review of research progress of hyper-heuristic algorithms

XIE Yi, HOU Yan’e, CHEN Xiaopan, KONG Yunfeng   

  1. College of Environment and Planning, Henan University, Kaifeng, Henan 475004, China
  • Online:2017-07-15 Published:2017-08-01


谢  毅,侯彦娥,陈小潘,孔云峰   

  1. 河南大学 环境与规划学院,河南 开封 475004

Abstract: Hyper-heuristics algorithms seek to efficiently solve the combinatorial optimization problems. It is expected to handle classes of problems rather than solving just one problem. This paper presents a systematic review of the research progress of hyper-heuristic algorithms. Based on a brief introduction to hyper-heuristic definition, structure and classification, the heuristic selection and heuristic generation approaches of hyper-heuristics are summarized. The techniques such as the machine learning methods in choosing low-level heuristics, the move acceptance criterions for neighborhood solutions, the generation of low-level heuristics, and the algorithm framework are discussed in details. Finally, the limitations and further research directions of hyper-heuristics algorithms are discussed.

Key words: hyper-heuristics algorithm, selection, generation

摘要: 超启发算法是一类新兴的优化方法,通过机器学习、算法选择、算法生成等技术求解组合优化等问题,具备跨问题领域求解的能力。针对超启发算法研究进展进行综述和讨论。首先,梳理超启发算法的定义、结构、特点和分类;其次,归纳选择式超启发算法和生成式超启发算法的研究进展及相关技术,包括选择低层启发式算法采用的学习方法,迭代计算中的移动接受策略,低层启发式算法的生成方法;最后,讨论现有超启发算法研究中存在的不足及未来的研究方向。

关键词: 超启发算法, 选择式, 生成式