基于粒子群理论的鱼群聚集模型——实现鱼群聚集全过程的自动化建模

Fish Aggregation Model Based on Particle Swarm Theory—Automatic Modeling of the Whole Process of Fish Aggregation

随着复杂性科学和群体智能算法的快速发展,群体行为这一基础研究领域逐渐被重视。本文讨论了一类在自然界较为广泛的群体行为,即鱼群的运动和聚集过程。该过程是群体动力学研究的热点方向,但大多研究仅面向鱼群聚集的部分过程,因此本文面向全过程提出了一类依据粒子群理论的有效模型。为了更好地模拟鱼群的聚集,本文将这一过程按系统的有序程度划分为不稳定状态和稳定状态,并对这2种状态分别进行建模。首先,本文使用了一类具有视野域和差异化速率的Boid模型来刻画鱼群的不稳定状态,然后使用了粒子环绕模型来刻画鱼群的稳定状态,弥补了Boid模型的缺陷。最后,模型的仿真结果表明,它较为有效地模拟了鱼群的聚集过程,且容易从二维空间拓展至三维空间中。

With the rapid development of complexity science and swarm intelligence algorithm, the basic research field of group behavior has been paid more and more attention. This paper discusses a kind of group behavior which is widely used in nature, that is, the movement and aggregation of fish. This process is a hot topic in the research of population dynamics, but most of the researches only focus on the partial process of fish aggregation. Therefore, this paper proposes an effective model based on particle swarm optimization theory for the whole process. In order to better simulate the aggregation of fish, the process is divided into unstable state and stable state according to the order degree of the system, and the two states are modeled respectively. Firstly, this paper uses a kind of Boid Model with field of view and differentiation rate to describe the unstable state of the fish swarm, and then uses the particle surround Model to describe the stable state of the fish swarm, which makes up for the defects of the Boid Model. Finally, the simulation results of the model show that it can effectively simulate the aggregation process of fish swarm, and it is easy to expand from two-dimensional space to three-dimensional space.