An Intelligent Particle Swarm Optimization Model based on Multi-Agent System

Particle swarm optimization techniques are typically made up of a population of simple agents interacting locally with one another and with their environment, with the goal of locating the optima within the operational environment. In this paper, a robust and intelligent particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are incorporated into the particle agents to dynamically adjust their optimality behaviours. Autonomy is achieved by the use of communicators that separate an agent’s individual operation from that of the swarm, thereby making the system more robust.