Particle swarm optimization (PSO) is a popular meta-heuristic for black-box optimization. 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 algorithm which harnesses this new perspective to achieve a model and behavior which is completely distinct from the family of traditional PSO systems.

Additional Metadata
Keywords Meta-heuristic optimization, Particle swarm optimization, Swarm intelligence
Persistent URL dx.doi.org/10.1007/s10458-016-9350-8
Journal Autonomous Agents and Multi-Agent Systems
Citation
Bell, N. (Nathan), & Oommen, J. (2017). A novel abstraction for swarm intelligence: particle field optimization. Autonomous Agents and Multi-Agent Systems, 1–24. doi:10.1007/s10458-016-9350-8