Swarm Intelligence (SI) can therefore be defined as a relatively new branch of Artificial Intelligence that is used to model the collective behavior of social swarms in nature, such as ant colonies, honey bees, and bird flocks. Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. According to Bonabeau in 1999 swarm intelligence is “The emergent collective intelligence of groups of simple agents.”
Mostly inspired by biological systems, swarm intelligence adopts the collective behavior of an organized group of animals, as they strive to survive.
The social interactions among swarm individuals can be either direct or indirect. Examples of direct interaction are through visual or audio contact, such as the waggle dance of honey bees. Indirect interaction occurs when one individual changes the environment and the other individuals respond to the new environment, such as the pheromone trails of ants that they deposit on their way to search for food sources.
Analogies in IT and social insects
*distributed system of interacting autonomus agents
*goals: performance optimization and robustness
*self-organized control and cooperation (decentralized)
*division of labor and distributed task allocation
*indirect interactions
In the late-80s, computer scientists proposed the scientific insights of these natural swarm systems to the field of Artificial Intelligence. In 1989, the expression "Swarm Intelligence" was first introduced by G. Beni and J. Wang in the global optimization framework as a set of algorithms for controlling robotic swarm.
There are multiple reasons responsible for the growing popularity of such SI-based algorithms, most importantly being the flexibility and versatility offered by these algorithms. The self-learning capability and adaptability to external variations are the key features exhibited by the algorithms which has attracted immense interest and identified several application areas.
Swarm Intelligence algorithms in several optimization tasks and research problems. Swarm Intelligence principles have been successfully applied in a variety of problem domains including function optimization problems, finding optimal routes, scheduling, structural optimization, and image and data analysis. Two of the most popular and successful examples of the Swarm Intelligence approach are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
The basic units of a swarm should be capable of simple computation related to its surrounding environment. Here computation is regarded as a direct behavioral response to environmental variance, such as those triggered by interactions among agents. Depending on the complexity of agents involved, responses may vary greatly.
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