## Algorithms:

- Particle swarm optimization
- Ant system
- Ant colony system
- Bees algorithm
- Wasp swarm algorithm
- Ant-Lion Optimization
- Grey wolf optimization
- Moth-flame optimization algorithm
- Bacterial foraging optimization algorithm
- Fish-swarm algorithm
- Elephant Herding Optimization
- Cuckoo search optimization
- Bat algorithm
- Firefly algorithm
- Monarch butterfly optimization
- Chicken swarm optimization
- Krill herd
- Dragonfly Algorithm
- Glowworm swarm optimization
- Cat swarm optimization
- Cooperative Group Optimization
- Artificial swarm intelligence
- Harris hawks optimization
- Killer Whale Algorithm
- Emperor Penguins Colony

## Packages and tools:

## Introduction

Swarm intelligence is the study of computational systems inspired by the « collective intelligence ». Collective Intelligence emerges through the cooperation of large numbers of homogeneous agents in the environment. Examples include schools of fish, flocks of birds, and colonies of ants. Such intelligence is decentralized, self-organizing and distributed through out an environment. In nature such systems are commonly used to solve problems such as effective foraging for food, prey evading, or colony re-location. The information is typically stored throughout the participating homogeneous agents, or is stored or communicated in the environment itself such as through the use of pheromones in ants, dancing in bees, and proximity in fish and birds.

The paradigm consists of two dominant sub-fields 1) Ant Colony Optimization that investigates probabilistic algorithms inspired by the stigmergy and foraging behavior of ants, and 2) Particle Swarm Optimization that investigates probabilistic algorithms inspired by the flocking, schooling and herding. Like evolutionary computation, swarm intelligence algorithms or strategies are considered adaptive strategies and are typically applied to search and optimization domains.