Swarm behaviour

Auklet flock Shumagins 1986

Metric vs topological distance in schools of fish

Sort sol ved Ørnsø 2007

Linear clusters of Ampyx priscus 2

File:Mueckenschwarm 50p 1600kbit.ogv Swarm behaviour or swarm intelligence is the collective motion and decision-making process of a large number of self-organized entities. It is most commonly observed in the animal kingdom, such as in birds, fish, termites, and bees. These entities follow simple rules, and although there is no centralized control structure dictating how individual members should behave, local, and to a certain degree random, interactions between such individuals lead to the emergence of "intelligent" global behavior, unknown to the individual members.

Overview

Swarm behaviour is a phenomenon that emerges when a group of simple organisms cooperate in an environment, creating patterns that seem to have a degree of intelligence. The underlying principle of swarm intelligence is that the collective behaviors of (often simple) agents interacting locally with their environment cause coherent functional global patterns to emerge. This concept is applied in work on artificial intelligence (AI), particularly in the fields of robotics and algorithm development, such as optimization algorithms and machine learning.

Characteristics

Swarm behaviour is characterized by several key features:

• Decentralization: There is no single leader or centralized decision-making body; decisions are made through consensus or the aggregate movement of the swarm.
• Flexibility: The swarm can quickly adapt to changing environments or threats.
• Robustness: The swarm can sustain the loss of several individuals without significant loss of functionality.
• Self-organization: Rules followed by individuals lead to the emergence of structured behavior on a global scale without external control.

Examples in Nature

• Birds: Flocking birds, such as starlings, exhibit complex flight patterns and seemingly coordinated movements, known as murmurations.
• Fish: Schools of fish move together, changing direction sharply, to avoid predators and to forage.
• Termites: Termites build complex mounds through coordinated efforts, despite having no blueprint or leader.
• Bees: Bees demonstrate swarm intelligence through their foraging behavior, decision-making in selecting a new hive location, and temperature regulation of the beehive.

Applications

Swarm intelligence principles have been applied in various fields, including:

• Robotics: Developing groups of robots that can work together to accomplish tasks without central control.
• Computer Science: In algorithms for optimization problems, such as the Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO).
• Telecommunications: Managing networks and routing protocols by mimicking the principles of swarm behaviour.

Challenges and Future Directions

While swarm behaviour offers promising solutions to complex problems, there are challenges in translating these behaviors from nature to technology. These include designing individual agents, defining interaction rules that lead to desired global behaviors, and ensuring robustness and flexibility in the face of unexpected challenges. Future research directions involve deeper understanding of natural swarms, improving algorithms based on swarm intelligence, and exploring new applications in AI, robotics, and beyond.