International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1270 - 1281

A Hybrid Global Optimization Algorithm Based on Particle Swarm Optimization and Gaussian Process

Authors
Yan Zhang1, 2, Hongyu Li1, 3, *, Enhe Bao1, Lu Zhang1, 4, Aiping Yu1
1College of Civil Engineering and Architecture, Guilin University of Technology, Guilin 541004, China
2Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin 541004, China
3Collaborative Innovation Center for Exploration of Hidden Nonferrous Metal Deposits and Development of New Materials in Guangxi, Guilin University of Technology, Guilin 541004, China
4Department of Civil and Materials Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
*Corresponding author. E-mail: lihongyu@glut.edu.cn
Corresponding Author
Hongyu Li
Received 22 April 2019, Accepted 28 October 2019, Available Online 12 November 2019.
DOI
10.2991/ijcis.d.191101.004How to use a DOI?
Keywords
Swarm optimization; Gaussian process; Global optimization; Surrogate approach
Abstract

The optimization problems and algorithms are the basics subfield in artificial intelligence, which is booming in the almost any industrial field. However, the computational cost is always the issue which hinders its applicability. This paper proposes a novel hybrid optimization algorithm for solving expensive optimizing problems, which is based on particle swarm optimization (PSO) combined with Gaussian process (GP). In this algorithm, the GP is used as an inexpensive fitness function surrogate and a powerful tool to predict the global optimum solution for accelerating the local search of PSO. In order to improve the predictive capacity of GP, the training datasets are dynamically updated through sorting and replacing the worst fitness function solution with the better solution during the iterative process. A numerical study is carried out using twelve different benchmark functions with 10, 20 and 30 dimensions, respectively. Regarding solving of the ill-conditioned computationally expensive optimization problems, results show that the proposed algorithm is much more efficient and suitable than the standard PSO alone.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1270 - 1281
Publication Date
2019/11/12
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191101.004How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Yan Zhang
AU  - Hongyu Li
AU  - Enhe Bao
AU  - Lu Zhang
AU  - Aiping Yu
PY  - 2019
DA  - 2019/11/12
TI  - A Hybrid Global Optimization Algorithm Based on Particle Swarm Optimization and Gaussian Process
JO  - International Journal of Computational Intelligence Systems
SP  - 1270
EP  - 1281
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191101.004
DO  - 10.2991/ijcis.d.191101.004
ID  - Zhang2019
ER  -