What is Swarm Intelligence?

Swarm Intelligence is a paradigm where a collection of simple, autonomous entities interact to create a group that exhibits complex behavior.


What it is:

Swarm intelligence is defined as a paradigm where a collection of simple, autonomous entities (such as in nature) interact creating a group that exhibits complex behavior.

Why it matters:

When applied to technology, this paradigm allows us to achieve complex outcomes with limited code, processing power, and hardware requirements.

What to do about it:

Technologists and business leaders should be aware of the possible applications of swarm intelligence to transform various fields such as robotics, data mining, medicine, and blockchains, and explore how their own projects and teams may benefit.

Observation in Nature and Application in Tech

Ants, birds, and water droplets are simple creatures, yet when many of them work together, they form colonies, flocks, and rivers, all of which display incredibly complex behavior. These simple creatures possess swarm intelligence.

Researchers observe swarm intelligence in nature and extrapolate how it could solve real-world problems. For example, an ant’s swarm intelligence is extremely effective in applications ranging from vehicle routing to job scheduling, to bankruptcy prediction. Beehive intelligence is well suited for factory optimization, particle clustering, and hydrothermal dispatching.

By studying the group, researchers then determine the rules the individuals use. For example, at the most basic level, birds need only three behavior (rules) to form a flock:

  1. Don’t crowd other birds.
  2. Steer towards the average heading of other birds.
  3. Move towards the center of the group.

These rules are easily modeled in an algorithm. The algorithm is then enhanced for real-world application and then deployed. The principle plays well with object-oriented programming techniques, in which autonomous instances are made of classes of object – mapping onto the notion of the swarm.

Implementation Timeline and Examples

Swarm intelligence has been implemented in real life scenarios since the late 1980s and is now being implemented in a wide variety of industries and applications. Nonetheless, advances continue to be made and the technology still has vast potential.

Swarm Intelligence is being implemented in countless ways. Experts estimate nearly 30 nations are developing drone swarms for military missions, including intelligence gathering, missile defense, precision missile strikes, and enhanced communication. While uses are broad, the table below highlights specific usage patterns including:

  • Routing and scheduling, for example traffic and workflows
  • Optimization, such as people and resource management
  • Analytics, to identify patterns and deliver insights
  • Design, in terms of technology architecture, animation and pharmaceutical

NASA is also looking at deploying swarms of tiny spacecraft for space exploration, and swarms of nanobots could soon be deployed by the medical community for things such as: precision delivery of drugs, microsurgery, targeting toxins, and biological sensors.

More examples include:

Routing Optimization Analytics Design
Grid Workflow Scheduling Aircraft Boarding Consumer Feedback Computer Animation
Autonomous Vehicle Routing Combinatorial Data Mining Inhibitory Peptide Design
Ground Traffic Control Reservoir Optimization Bankruptcy Prediction Network Design
Multi-Depot Vehicle Routing Distributed Information Retrieval Climate Change Intervention Circuit Design

Take-Away Actions

While swarm intelligence can be seen as an emerging technology, it can already offer a response to challenging issues in the enterprise and beyond. Whether an organization is looking to address inefficiency or deliver more effectively, swarm intelligence can be deployed as a one-off solution or as part of a broader response. It is worth looking at examples such as the above, and identifying adjacencies with existing requirements.