Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem

Zhiming Zhang; Qingya Sui; Lingyu Qi; Yaotong Song; Shangce Gao

doi:10.1109/ITAIC58329.2023.10408765

Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem

Zhiming Zhang, Qingya Sui, Lingyu Qi^*, Yaotong Song, Shangce Gao^*

^*Corresponding author for this work

Intellectual Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches.

Original language	English
Title of host publication	IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1357-1361
Number of pages	5
ISBN (Electronic)	9798350333664
DOIs	https://doi.org/10.1109/ITAIC58329.2023.10408765
State	Published - 2023
Event	11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023 - Chongqing, China Duration: 2023/12/08 → 2023/12/10

Publication series

Name	IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
ISSN (Print)	2693-2865

Conference

Conference	11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023
Country/Territory	China
City	Chongqing
Period	2023/12/08 → 2023/12/10

Keywords

Meta-heuristic
Optimization problem
Swarm intelligence algorithm

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Access to Document

10.1109/ITAIC58329.2023.10408765

Cite this

Zhang, Z., Sui, Q., Qi, L., Song, Y., & Gao, S. (2023). Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem. In B. Xu, & K. Mou (Eds.), IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (pp. 1357-1361). (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITAIC58329.2023.10408765

Zhang, Zhiming ; Sui, Qingya ; Qi, Lingyu et al. / Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem. IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1357-1361 (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)).

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title = "Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem",

abstract = "Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches.",

keywords = "Meta-heuristic, Optimization problem, Swarm intelligence algorithm",

author = "Zhiming Zhang and Qingya Sui and Lingyu Qi and Yaotong Song and Shangce Gao",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023 ; Conference date: 08-12-2023 Through 10-12-2023",

year = "2023",

doi = "10.1109/ITAIC58329.2023.10408765",

language = "英語",

series = "IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1357--1361",

editor = "Bing Xu and Kefen Mou",

booktitle = "IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference",

}

Zhang, Z, Sui, Q, Qi, L, Song, Y & Gao, S 2023, Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem. in B Xu & K Mou (eds), IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Institute of Electrical and Electronics Engineers Inc., pp. 1357-1361, 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023, Chongqing, China, 2023/12/08. https://doi.org/10.1109/ITAIC58329.2023.10408765

Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem. / Zhang, Zhiming; Sui, Qingya; Qi, Lingyu et al.
IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1357-1361 (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem

AU - Zhang, Zhiming

AU - Sui, Qingya

AU - Qi, Lingyu

AU - Song, Yaotong

AU - Gao, Shangce

PY - 2023

Y1 - 2023

N2 - Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches.

AB - Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches.

KW - Meta-heuristic

KW - Optimization problem

KW - Swarm intelligence algorithm

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U2 - 10.1109/ITAIC58329.2023.10408765

DO - 10.1109/ITAIC58329.2023.10408765

M3 - 会議への寄与

AN - SCOPUS:85186070332

T3 - IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

SP - 1357

EP - 1361

BT - IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference

A2 - Xu, Bing

A2 - Mou, Kefen

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

Y2 - 8 December 2023 through 10 December 2023

ER -

Zhang Z, Sui Q, Qi L, Song Y, Gao S. Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem. In Xu B, Mou K, editors, IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1357-1361. (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)). doi: 10.1109/ITAIC58329.2023.10408765

Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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