2012年8月17日 · By tricking live fish into attacking computer-generated "prey," scientists have learned that animals like birds and fish may indeed have evolved to swarm together to protect themselves from...

The term flocking or murmuration can refer specifically to swarm behaviour in birds, herding to refer to swarm behaviour in tetrapods, and shoaling or schooling to refer to swarm behaviour in fish. Phytoplankton also gather in huge swarms called blooms, although these organisms are algae and are not self-propelled the way animals are.

2024年5月21日 · 人工鱼群算法（Artificial Fish Swarm Algorithm, AFSA）是一种基于仿生学的群体智能优化算法，通过模拟鱼类在水中觅食、群聚和避碰等行为来进行全局优化。 它由中国学者李海涛等人在2002年提出，作为一种新的仿生计算技术， AFSA 在解决复杂的多目标优化问题上表现 ...

2019年11月15日 · 人工鱼群算法（Artificial Fish Swarm Algorithm，简称AFSA）是李晓磊等人于2002年提出的一类基于动物行为的群体智能优化算法。 该 算法 是通过模拟鱼类的觅食、聚群、追尾、随机等行为在搜索域中进行寻优,是集 群体智能 思想的一个具体应用。

在一片水域中， 鱼往往能自行或尾随其他鱼找到营养物质多的地方，因而鱼生存数目最多的地方一般就是本水域中营养物质最多的地方，人工鱼群算法就是根据这一特点，通过构造人工鱼来模仿鱼群的觅食、聚群及追尾行为，从而实现寻优。 人工鱼拥有以下几种典型行为: (1)觅食行为： 一般情况下鱼在水中随机地自由游动,当发现食物时,则会向食物逐渐增多的方向快速游去。 (2)聚群行为: 鱼在游动过程中为了保证自身的生存和躲避危害会自然地聚集成群，鱼聚群时所遵守的规则有 …

2021年1月13日 · Now, a team of researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering have developed fish-inspired robots that can synchronize their movements like a real school of fish, without any external control.

2024年12月8日 · 人工鱼群算法（Artificial Fish Swarm Algorithm, AFSA）是一种基于生物群体行为的优化算法，由吴宏基等人于2002年提出。 该 算法 模拟了自然界中鱼群觅食的行为，通过个体间的交互机制来寻找全局最优解。

2020年11月11日 · Abstract: The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged ...

鱼群算法（Fish Swarm Algorithm，FSA）是一种基于仿生学原理的群智能算法，模拟了鱼群在水中集群、觅食和逃避掠食者等行为。 它是一种全局优化算法，主要用于解决复杂的非线性优化问题。

The Articial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of sh schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include exibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an eective Swarm