鸟群算法入门：生物启发式优化的新方法

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本文档深入探讨了一种新兴的生物启发式优化算法——鸟群算法(Bird Swarm Algorithm)，为对这个领域感兴趣的初学者提供了一个优秀的入门材料。鸟群算法是模仿自然界中鸟类觅食行为的一种计算智能方法，它借鉴了鸟类在群体中的协同搜索、信息共享和领导力等特点，将这些特性转化为求解复杂问题的数学模型。首先，作者Xian-Bing Meng、X.Z. Gao、Lihua Lu、Yu Li、Hengzhen Zhang来自多个知名学府，如上海海事大学、成都绿色能源与绿色制造研发中心、Aalto大学和复旦大学等，他们共同合作，旨在推动该领域的研究和应用。文章发表于《实验与理论人工智能杂志》(Journal of Experimental & Theoretical Artificial Intelligence)，表明其研究成果具有较高的学术价值。鸟群算法的核心思想包括： 1. **模仿行为**：算法模拟鸟类在天空中的飞行行为，个体通过模仿其他鸟类的路径和速度来寻找最佳解决方案。这体现了分布式决策和群体智慧的概念。 2. **领导角色**：存在一个或多个“领袖”鸟类，它们可以提供局部最优解的信息，引导整个群体朝着全局最优方向前进。 3. **信息交流**：个体之间的信息交换有助于共享经验和避免重复探索，提高了搜索效率。 4. **动态调整**：鸟群的搜索策略会随着环境和任务的变化而动态调整，确保算法的适应性和鲁棒性。 5. **优化过程**：通过迭代和竞争机制，鸟群算法能够在解决诸如函数优化、机器学习、工程设计等问题时，寻找到接近最优的解。文章详细介绍了算法的具体实现步骤，包括初始化、位置更新、速度调整、领航者更新以及终止条件等关键环节。此外，为了评估算法的有效性，文中可能包含了针对特定问题的实验结果分析，展示了算法在实际优化任务中的性能。这篇论文不仅提供了鸟群算法的基础理论，还为读者展示了如何将这一理念应用于实际问题中，是一份宝贵的参考资料，对于希望在这个领域进一步学习和实践的科研人员和技术人员来说，具有很高的参考价值。阅读本文将有助于理解生物启发式优化方法的基本原理，提升优化技术的实践能力。

Jasni, 2013), Ant Colony Optimization (Dorigo & Stutzle, 2004), Artiﬁcial Fish Swarm

Algorithm (Gao, Wu, Zenger, & Huang, 2010), Artiﬁcial Bee Colony (Karaboga & Basturk,

2007), Glowworm Swarm Optimization (Krishnanand & Ghose, 2009), Cuckoo Search (Yang &

Deb, 2014), Bat Algorithm (Yang, 2010), Krill Herd (Gandomi & Alavi, 2012), etc. were

inspired by the swarm intelligence of social animals. Inspired by the plant, Invasive Weed

Optimization (Mehrabian & Lucas, 2006) and Flower Pollination Algorithm (Yang,

Karamanoglu, & He, 2014 ) were designed. Harmony Search (Manjarres et al., 2013), Charged

System Search (Kaveh & Talatahari, 2010), Brain Storm Optimization (Shi, 2011), etc. were

inspired by physical principles and nature phenomena.

Though many algorithms were developed to deal with optimisation applications, there still

exists no universal algorithm. Thus, the research of ﬁnding more efﬁcient algorithms is still in

progress.

In this paper, a new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is

proposed as a new optimisation method. BSA is simpliﬁcation of the social behaviours and

social interactions in bird swarms. It mimics the birds’ foraging behaviour, vigilance behaviour

and ﬂight behaviour. Thus, the swarm intelligence can be efﬁci ently extracted from the bird

swarms to optimise problems.

The rest of the paper is organised as follows. Section 2 illustrates the details of BSA.

Simulations and comparisons are presented in Section 3. Finally, some conclusions and

discussions are given in Section 4.

2. The Bird Swarm Algorithm

2.1 Biological fundamentals

Many bird species are gregarious, such as ﬁnches. They may roost communally, forage and ﬂy in

ﬂocks (Anderson, 2006). These behaviour s are considered as emergent behaviour s arising from

simple rules such as separation, alignment and cohesion. Through the simplest social interaction,

the swarm behaviours can develop complex motions and interactions.

Foraging in ﬂocks, birds may get more information than their own sense s can gather, and

have survival advantage and good foraging efﬁciency. If one bird ﬁnds some food patches,

others may feed from them (Kennedy, Eberhart, & Shi, 2001). While foraging, birds often

aggregate in response to predation threat (Krause & Ruxton, 2002). They frequently raise their

heads and scan their surroundings. These behaviours, interpreted as vigilance behaviour

(Anderson, 2006), may be conducive to detecting predators (Lima & Dill, 1990). Studies

showed that birds would randomly choose between foraging and keeping vigilance (Bednekoff

& Lima, 1998). Birds often give alarm calls when they detect a predator (Pulliam, Pyke, Caraco,

& Pulliam, 1982). Thus, the whole group would ﬂy off together. It can reasonably conclude that

birds in a ﬂock have a better chance of detecting a potential threat than a single one. A reduction

in individual vigilance with an increase in group size is so widespread in many birds (Ekman,

1987; Elgar & Catterall, 1981; Sullivan, 1984). In other words, a bird can spend more time

foraging as group size increases without affecting the increased risk of being attacked by the

predator (Beauchamp, 1998; Roberts, 2003).

The birds on the periphery of a group have more chance of being attacked by the predators

than those in the centre. Studies suggested that animals foraging in the centre of ﬂock may move

to their neighbours to protect themselves from being attacked by the predators (Pulliam, 1973).

Each bird would try to move towards the centre of the ﬂock as they perceive it. This motion,

however, may be affected by the interference induced by competing among the bird swarms

(Beauchamp, 2003). Thus, birds may not directly move towards the centre of the swarm.

X.-B. Meng et al.2

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