Perceptive particle swarm optimisation: an investigation

摘要

Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for real-world problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. Recently, the perceptive particle swarm optimisation (PPSO) algorithm was proposed to mimic behaviours of social animals more closely through both social interaction and environmental interaction for applications such as robot control. In this study, we investigate the PPSO algorithm on complex function optimisation problems and its ability to cope with noisy environments.