Types of Swarm Intelligence

• Particle Swarm Optimization

• Bee’s Algorithm

• Ant Colony Optimization

• Gravitational Search Algorithm

• Bacterial Foraging Optimization Algorithm

Swarm intelligence is a complex discipline that seeks to understand and replicate both natural and artificial systems that are composed of several parts but effectively coordinate their activities through decentralized control. This type of intelligence is often displayed in nature through the habits of animals like bees, fish, birds, and ants.

1. Particle Swarm Optimization

This type of algorithm is inspired by the flocking behavior of birds and the schooling behavior of fish in nature. The goal of this type of algorithm is to have all the components locate a common goal and then gradually advance towards the position while exploring the various positions in a boundary. Each component must not collide with others, align their own heading to the average of other components and move towards the average position of the components. This algorithm is primarily applied in areas like robotics. Innovative researchers at Wyss Institute at Harvard have applied this type of intelligence to RoboBees, autonomous flying drones.

2. Bee’s Algorithm

The bee’s algorithm was inspired by the unbelievable behavior of natural honey bees. In bee colonies, the hive will send out Scout bees. Scouts locate nectar, return to the hive and communicate their findings with other bees through a wiggle dance. The goal of a bee’s algorithm is to locate and explore various sites within a boundary. One example of the application of this algorithm is UNU, a software platform developed by Unanimous AI. The platform assembles groups of individuals to make collective decisions, and it allows users to answer questions as a swarm. Tech Republic reported that predictions about events went from being about 40 percent accurate when guessing as individuals to about 70 percent accurate as a swarm prediction.

3. Ant Colony Optimization

Ant colony optimization algorithms are based on the actions of natural ants. These amazing creatures communicate amongst each other about good pathways through the use of pheromones. Individual ants make decisions about what paths to take based on the intensity and visibility of the pheromone trail. The algorithms inspired by these creatures attempt to solve the classical shortest path problems and large dataset clustering problems. This type of technique has many applications including assignment, routing, subset, scheduling, machine learning and bioinformatics problems. Research Gate explains how ant colony optimization is currently being used alongside machine learning to diagnose lung cancer with a greater efficiency than alternative methods.

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4. Gravitational Search Algorithm

This mind-bending algorithm is a new type of approach based on Newton’s concept of gravity. According to this theory, every particle in the entire universe is attracted to every other particle with a directly proportional force. Algorithms inspired by this approach seek to have masses interact with each other based on the laws of motion while gravitating towards objects with heavier masses. While this approach was developed only recently in 2009, researchers have favored its application in areas like parameter identification, engineering, clustering, and classification.

5. Bacterial Foraging Optimization Algorithm

This algorithm was inspired by the bacteria E. Coli. E. Coli bacteria forages in a way that it allows it to move towards or away from specific signals by perceiving chemical gradients in the area. This incredible process inspired the algorithm which attempts to allow components to swarm towards the target. One type of application this algorithm is being used for is groundwater prediction methods. Science Direct explains another use for the application is in reducing the power loss and improving the voltage of radial distribution systems.

In addition to these examples of hive-like intelligence, scientists have developed a number of other technologies and algorithms based on nature’s unique way of solving issues. Research into swarm intelligence is helping society find solutions to challenging and complex computational problems, and it will continue to be a major focal point for advancements in the future.