群体智能
群体行为
计算机科学
觅食
水准点(测量)
算法
粒子群优化
理论(学习稳定性)
人工智能
社会行为
机器学习
生态学
地理
大地测量学
生物
心理学
社会心理学
作者
Xian-Bing Meng,X.Z. Gao,Lihua Lü,Yu Liu,Hengzhen Zhang
标识
DOI:10.1080/0952813x.2015.1042530
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.
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