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超启发算法研究进展综述
引用本文:谢 毅,侯彦娥,陈小潘,孔云峰.超启发算法研究进展综述[J].计算机工程与应用,2017,53(14):1-8.
作者姓名:谢 毅  侯彦娥  陈小潘  孔云峰
作者单位:河南大学 环境与规划学院,河南 开封 475004
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  

Review of research progress of hyper-heuristic algorithms
XIE Yi,HOU Yan’e,CHEN Xiaopan,KONG Yunfeng.Review of research progress of hyper-heuristic algorithms[J].Computer Engineering and Applications,2017,53(14):1-8.
Authors:XIE Yi  HOU Yan’e  CHEN Xiaopan  KONG Yunfeng
Affiliation:College of Environment and Planning, Henan University, Kaifeng, Henan 475004, China
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.
Keywords:hyper-heuristics algorithm  selection  generation  
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