Particle Field Optimization: A New Paradigm for Swarm Intelligence

Particle Swarm Optimization (PSO) has been a popular meta-heuristic for black-box optimization for almost two decades. In essence, within this paradigm, the system is fully defined by a swarm of “particles” each characterized by a set of features such as its position, velocity and acceleration. The consequent optimized global best solution is obtained by comparing the personal best solutions of the entire swarm. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we submit a new, abstracted, perspective of the PSO system, where we attempt to move away from the swarm of individual particles, but rather characterize each particle by a field or distribution. The strategy that updates the various fields is akin to Thompson’s sampling. By invoking such an abstraction, we present the novel Particle Field Optimization (PFO) algorithm which harnesses this new perspective to achieve a model and behavior completely distinct from the family of traditional PSO systems. Categories: G.1.6 [Optimization]: Global optimization; I.2.11 [Distributed AI]: Multiagent systems