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

来源：

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE | 2016年 / 28卷 / 04期

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

页码：673 / 687

页数：15

共 50 条

[21] A Model for Bio-Inspired Underwater Swarm Robotic Exploration
Varughese, Joshua Cherian
Thenius, Ronald
Leitgeb, Paul
Wotawa, Franz
Schmickl, Thomas
IFAC PAPERSONLINE, 2018, 51 (02): : 385 - 390
[22] Bio-inspired BAT optimization Algorithm for Handwritten Arabic Characters Recognition
Sahlol, Ahmed T.
Suen, Ching Y.
Zawbaa, Hossam M.
Abd Elfattah, Mohamed
Hassanien, Aboul Ella
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 1749 - 1756
[23] Particle Swarm Optimization with Improved Bio-inspired Bees
Tayebi, Mohammed
Baba-Ali, Ahmed Riadh
MODELLING, COMPUTATION AND OPTIMIZATION IN INFORMATION SYSTEMS AND MANAGEMENT SCIENCES - MCO 2015 - PT II, 2015, 360 : 197 - 208
[24] Two New Bio-Inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours
Varna, Fevzi Tugrul
Husbands, Phil
BIOMIMETICS, 2024, 9 (09)
[25] Swarm intelligence-based bio-inspired algorithms
Bozhinoski, Darko
PROCEEDINGS OF THE 2024 IEEE/ACM 19TH SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, SEAMS 2024, 2024, : 105 - 106
[26] Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems
Bouaouda A.
Hashim F.A.
Sayouti Y.
Hussien A.G.
Neural Computing and Applications, 2024, 36 (25) : 15455 - 15513
[27] Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm
Schmickl, T.
Crailsheim, K.
AUTONOMOUS ROBOTS, 2008, 25 (1-2) : 171 - 188
[28] Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm
T. Schmickl
K. Crailsheim
Autonomous Robots, 2008, 25 : 171 - 188
[29] Bio-inspired Strategies for the Coordination of a Swarm of Robots in an Unknown Area
Palmieri, Nunzia
de Rango, Floriano
Yang, Xin She
Marano, Salvatore
COMPUTATIONAL INTELLIGENCE, IJCCI 2015, 2017, 669 : 96 - 112
[30] Chaotic Bird Swarm Optimization Algorithm
Ismail, Fatma Helmy
Houssein, Essam H.
Hassanien, Aboul Ella
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2018, 2019, 845 : 294 - 303

← 1 2 3 4 5 →