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Swarm Intelligence in Autonomic Computing: The Particle Swarm Optimization Case
Abstract
Autonomic computing has become increasingly popular during recent years. Many mobile autonomic and context-aware applications exhibit self-organization in dynamic environments adopted from multi-agent, or swarm, research. The basic paradigm behind swarm systems is that tasks can be more efficiently dispatched through the use of multiple, simple autonomous agents instead of a single, sophisticated one. Such systems are much more adaptive, scalable, and robust than those based on a single, highly capable, agent. A swarm system can generally be defined as a decentralized group (swarm) of autonomous agents (particles) that are simple, with limited processing capabilities. Particles must cooperate intelligently to achieve common tasks.
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