A new bio-inspired optimisation algorithm: Bird Swarm Algorithm

被引:324
|
作者
Meng, Xian-Bing [1 ,2 ]
Gao, X. Z. [3 ]
Lu, Lihua [4 ,5 ]
Liu, Yu [2 ]
Zhang, Hengzhen [1 ]
机构
[1] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China
[2] Chengdu Green Energy & Green Mfg R&D Ctr, Chengdu, Peoples R China
[3] Aalto Univ, Dept Elect Engn & Automat, Sch Elect Engn, Aalto, Finland
[4] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China
[5] Zhengzhou Univ Light Ind, Coll Math & Informat Sci, Zhengzhou, Peoples R China
关键词
bird swarms; swarm intelligence; social behaviours; social interactions; Bird Swarm Algorithm; optimisation; GROUP-SIZE; HOUSE SPARROWS; PRODUCER; ADVANTAGES; PREDATION; VIGILANCE; FLOCKS; TIME; RISK;
D O I
10.1080/0952813X.2015.1042530
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications. BSA is based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms. Birds mainly have three kinds of behaviours: foraging behaviour, vigilance behaviour and flight behaviour. Birds may forage for food and escape from the predators by the social interactions to obtain a high chance of survival. By modelling these social behaviours, social interactions and the related swarm intelligence, four search strategies associated with five simplified rules are formulated in BSA. Simulations and comparisons based on eighteen benchmark problems demonstrate the effectiveness, superiority and stability of BSA. Some proposals for future research about BSA are also discussed.
引用
收藏
页码:673 / 687
页数:15
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