Nature-Inspired Intelligent Computing: A Comprehensive Survey

被引:0
|
作者
Jiao, Licheng [1 ]
Zhao, Jiaxuan [1 ]
Wang, Chao [1 ]
Liu, Xu [1 ]
Liu, Fang [1 ]
Li, Lingling [1 ]
Shang, Ronghua [1 ]
Li, Yangyang [1 ]
Ma, Wenping [1 ]
Yang, Shuyuan [1 ]
机构
[1] Xidian Univ, Sch Artificial Intelligence, Xian, Peoples R China
关键词
IMPERIALIST COMPETITIVE ALGORITHM; CUCKOO SEARCH ALGORITHM; ELECTROMAGNETISM-LIKE MECHANISM; COYOTE OPTIMIZATION ALGORITHM; LEARNING-BASED OPTIMIZATION; ARTIFICIAL IMMUNE-SYSTEM; ANT COLONY OPTIMIZATION; WATER CYCLE ALGORITHM; PARAMETER-IDENTIFICATION; EVOLUTIONARY ALGORITHM;
D O I
10.34133/research.0442
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.
引用
收藏
页数:38
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