Multi-objective Particle Swarm Optimization

Swarm Intelligence (SI) refers to the collective behaviour of a group of creatures without a centralized unit control. This field was first established in 1989 in a robotic project [1]. Systems built based on SI typically have independent intelligent agents that interact locally to achieve a goal as a team [2]. Most of the algorithms in this field mimic swarm intelligence in nature. For instance, Ant Colony Optimization (ACO) [3] mimics swarm intelligence of ants in an ant colony using stigmergy, which is the communication between individuals in a swarm by modifying environment. It has been proved that ants can find the shortest path between multiple path to a food course from their nest by a depositing and marking the ground using pheromone