Related papers: Directed particle swarm optimization with Gaussian-process-based function forecasting

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

URL: http://arxiv.org/abs/2102.04172v2
Date: Tue, 9 Feb 2021 08:31:07 GMT
Title: Directed particle swarm optimization with Gaussian-process-based function forecasting
Authors: Johannes Jakubik, Adrian Binding, Stefan Feuerriegel
Abstract summary: 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. We show that our algorithm attains desirable properties for exploratory and exploitative behavior.
Score: 15.733136147164032
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.