| 引用本文: | 王 凌,郑晓龙.果蝇优化算法研究进展[J].控制理论与应用,2017,34(5):557~563.[点击复制] |
| WANG Ling,ZHENG Xiao-long.Advances in fruit fly optimization algorithms[J].Control Theory and Technology,2017,34(5):557~563.[点击复制] |
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| 果蝇优化算法研究进展 |
| Advances in fruit fly optimization algorithms |
| 摘要点击 4677 全文点击 2968 投稿时间：2017-01-15 修订日期：2017-07-24 |
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| DOI编号 10.7641/CTA.2017.70030 |
| 2017,34(5):557-563 |
| 中文关键词 群智能 果蝇优化算法 知识驱动 协同 混合算法 |
| 英文关键词 swarm intelligence fruit fly optimization algorithm knowledge driven collaboration hybrid algorithm. |
| 基金项目 国家重点研发计划(2016YFB0901900)和国家杰出青年科学基金(61525304)资助. |
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| 中文摘要 |
| 作为一种新颖的群智能优化方法, 受基于视觉和嗅觉的觅食行为的启发而提出的果蝇优化算法具有易理
解和实现、控制参数少的特点. 近年来果蝇优化算法的研究受到了广泛关注, 果蝇优化算法及其变种在诸多工程优
化领域得到了成功应用. 阐述果蝇优化算法的设计思想与机制, 重点综述果蝇优化算法的研究进展, 包括维持种群
多样性、知识驱动策略与协同机制的设计等方面的改进工作. 同时, 介绍果蝇优化算法在离散优化、多目标优化、不
确定优化等方面的扩展性研究工作, 并总结果蝇优化算法的代表性应用研究成果, 最后指出在理论、设计、扩展、应
用等方面未来进一步的研究方向和内容. |
| 英文摘要 |
| As a novel swarm intelligence based optimization algorithm, the fruit fly optimization algorithm (FOA)
inspired by the foraging behavior of fruit flies with vision and smell is easy to understand, implement and has few control
parameters. During recent years, the research of the FOA has attracted wide attention, and the FOA and its variants have
gained successful applications in many engineering optimization fields. After stating the idea and mechanism to design
the FOA, the advances in the research of the FOA are surveyed in details, including the improvement work in terms of
maintaining the diversity of the population, designing the knowledge driven strategy and the collaborative mechanisms.
Moreover, the generalized research work of the FOA in the fields of discrete optimization, multi-objective optimization and
uncertain optimization are also introduced. In addition, the typical applications of the FOA are reviewed. Finally, some
future research directions and contents in terms of theory, design, extension and applications of the FOA are pointed out. |
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