A Bayesian Interpretation of the Particle Swarm Optimization and Its Kernel Extension

TL;DR

This paper offers a Bayesian framework for particle swarm optimization, enabling the incorporation of prior knowledge and kernel-based transformations to improve optimization performance.

Contribution

It introduces a formal Bayesian interpretation of PSO and extends it with kernel functions, unifying existing methods as special cases.

Findings

Provides a Bayesian formulation of PSO

Enables incorporation of prior knowledge

Extends PSO with kernel transformations

Abstract

Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved, such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases.