Directed particle swarm optimization with Gaussian-process-based function forecasting

TL;DR

This paper introduces a Gaussian-process-based surrogate model into particle swarm optimization, enhancing its efficiency by leveraging function forecasts, which outperforms traditional PSO and other surrogate-assisted algorithms on benchmarks.

Contribution

It presents a novel integration of Bayesian Gaussian process modeling into PSO, enabling better exploration and exploitation during optimization.

Findings

Outperforms baseline PSO (SPSO2011) in experiments.

Achieves significant improvements over state-of-the-art surrogate-assisted algorithms.

Demonstrates desirable exploratory and exploitative properties.

Abstract

Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function evaluations expensive. Yet the traditional PSO algorithm ignores the potential knowledge that could have been gained of the objective function from the observations by individual particles. Hence, we draw upon concepts from Bayesian optimization and introduce a stochastic surrogate model of the objective function. That is, we fit a Gaussian process to past evaluations of the objective function, forecast its shape and then adapt the particle movements based on it. Our computational experiments demonstrate that baseline implementations of PSO (i.e., SPSO2011) are outperformed. Furthermore, compared to, state-of-art surrogate-assisted evolutionary algorithms, we achieve substantial performance improvements on several popular benchmark functions. Overall, we find that our algorithm attains desirable properties for exploratory and exploitative behavior.