- Gray Wolves Optimization
- Bacterial feed optimization algorithm
- Fish school algorithm
- Optimization of elephant breeding
- Cuckoo Search Optimization
- Bat algorithm
- Chicken coop optimization
- Krill herd
- Cat swarm optimization
- Cooperative Group Optimization
- Artificial swarm intelligence
- Harris hawks optimization
- Killer Whale Algorithm
- Emperor Penguins Colony
Swarm intelligence (swarm algorithms) is the study of computer systems inspired by “collective intelligence”. Collective intelligence emerges through the cooperation of a large number of homogeneous agents in the environment. Examples include schools of fish, flocks of birds and ant colonies. This intelligence is decentralized, self-organized and distributed across an environment. In nature, such systems are commonly used to solve problems such as efficient foraging, prey escape, or colony displacement.
Information is usually stored in all participating homogeneous agents, or is stored or communicated in the environment itself, for example through the use of pheromones in ants, dancing in bees and proximity in fish and birds.
As a group, simple creatures following simple rules can display a surprising amount of complexity, efficiency and even creativity. Known as swarm intelligence, this trait is found in nature, but researchers have recently started using it to transform various fields such as robotics, data mining, medicine and blockchains.
Ants, for example, can perform only a limited range of functions, but an ant colony can build bridges, create food and information highways, wage war, and enslave other species of ants. – all of this is beyond the comprehension of a single ant. Similarly, schools of fish, flocks of birds, beehives and other species exhibit behavior indicative of a planning by a superior intelligence that does not really exist.
This happens through a process called stigmergy. Simply put, a small change by one member of the group causes the other members to behave differently, leading to a new pattern of behavior.
When an ant finds a food source, it marks the way with pheromones. This attracts other ants to this path, leads them to the food source, and prompts them to mark the same path with more pheromones. Over time, the most efficient route will become the highway, because the faster and easier a path, the more ants will reach the food and the more pheromones will be on the path. So, it seems that a smarter being chose the better path, but it came out of the tiny and simple changes made by individuals.
The paradigm consists of two dominant subdomains 1) Optimization colonies of ants which studies the probabilistic algorithms inspired by the stigmergy and foraging behavior of ants, and 2) Particle Swarm Optimization which investigates probabilistic algorithms inspired by herd, fish shoal, and herding. Like evolutionary computing, swarm intelligence algorithms or strategies are considered adaptive strategies and are generally applied to research and optimization domains.