Problem-Solving in Multi-Agent Systems: A Novel Generalized Particle Model

This paper presents a novel generalized particle model (GPM) for problem-solving in multi-agent systems (MAS). The construction, dynamics and properties of the GPA and corresponding algorithm are discussed. The GPA has many advantages in terms of the high-scale parallelism, multi-objective optimization, multi-type coordination, multi-degree autonomy, and the ability to deal randomly occurring phenomena in MAS systems