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

doi:10.1016/j.ejor.2021.02.053

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

2021-11-01

19

OA

AI

DOI：10.1016/j.ejor.2021.02.053

原文链接

查看原文

摘要

En 中文

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. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Keyword:

Forecasting

Gaussian process

Surrogate model

SPSO2011

Particle swarm optimization

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

摘要

AI总结

期刊

机构