No Cover Image

Journal article 234 views 64 downloads

Beetle Colony Optimization Algorithm and its Application

Heng Zhang, Ziming Li, Xiangyuan Jiang, Xiaojing Ma, Jiyang Chen, Shuai Li Orcid Logo, Yizhong Luan, Zhenyi Lv, Sile Ma

IEEE Access, Volume: 8, Pages: 128416 - 128425

Swansea University Author: Shuai Li Orcid Logo

  • 54990.pdf

    PDF | Version of Record

    Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY).

    Download (1.54MB)

Abstract

Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing appli...

Full description

Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa54990
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2020-08-17T09:08:54Z
last_indexed 2020-09-17T03:19:01Z
id cronfa54990
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2020-09-16T16:21:46.4422569</datestamp><bib-version>v2</bib-version><id>54990</id><entry>2020-08-17</entry><title>Beetle Colony Optimization Algorithm and its Application</title><swanseaauthors><author><sid>42ff9eed09bcd109fbbe484a0f99a8a8</sid><ORCID>0000-0001-8316-5289</ORCID><firstname>Shuai</firstname><surname>Li</surname><name>Shuai Li</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2020-08-17</date><deptcode>MECH</deptcode><abstract>Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning.</abstract><type>Journal Article</type><journal>IEEE Access</journal><volume>8</volume><paginationStart>128416</paginationStart><paginationEnd>128425</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><issnElectronic>2169-3536</issnElectronic><keywords/><publishedDay>23</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-07-23</publishedDate><doi>10.1109/access.2020.3008692</doi><url/><notes/><college>COLLEGE NANME</college><department>Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MECH</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-09-16T16:21:46.4422569</lastEdited><Created>2020-08-17T10:07:06.6733283</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Heng</firstname><surname>Zhang</surname><order>1</order></author><author><firstname>Ziming</firstname><surname>Li</surname><order>2</order></author><author><firstname>Xiangyuan</firstname><surname>Jiang</surname><order>3</order></author><author><firstname>Xiaojing</firstname><surname>Ma</surname><order>4</order></author><author><firstname>Jiyang</firstname><surname>Chen</surname><order>5</order></author><author><firstname>Shuai</firstname><surname>Li</surname><orcid>0000-0001-8316-5289</orcid><order>6</order></author><author><firstname>Yizhong</firstname><surname>Luan</surname><order>7</order></author><author><firstname>Zhenyi</firstname><surname>Lv</surname><order>8</order></author><author><firstname>Sile</firstname><surname>Ma</surname><order>9</order></author></authors><documents><document><filename>54990__17944__4e1c276ddbc143da9994056ceeff4407.pdf</filename><originalFilename>54990.pdf</originalFilename><uploaded>2020-08-17T10:08:47.1664663</uploaded><type>Output</type><contentLength>1616053</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>English</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2020-09-16T16:21:46.4422569 v2 54990 2020-08-17 Beetle Colony Optimization Algorithm and its Application 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-08-17 MECH Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning. Journal Article IEEE Access 8 128416 128425 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 23 7 2020 2020-07-23 10.1109/access.2020.3008692 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-09-16T16:21:46.4422569 2020-08-17T10:07:06.6733283 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Heng Zhang 1 Ziming Li 2 Xiangyuan Jiang 3 Xiaojing Ma 4 Jiyang Chen 5 Shuai Li 0000-0001-8316-5289 6 Yizhong Luan 7 Zhenyi Lv 8 Sile Ma 9 54990__17944__4e1c276ddbc143da9994056ceeff4407.pdf 54990.pdf 2020-08-17T10:08:47.1664663 Output 1616053 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY). true English https://creativecommons.org/licenses/by/4.0/
title Beetle Colony Optimization Algorithm and its Application
spellingShingle Beetle Colony Optimization Algorithm and its Application
Shuai Li
title_short Beetle Colony Optimization Algorithm and its Application
title_full Beetle Colony Optimization Algorithm and its Application
title_fullStr Beetle Colony Optimization Algorithm and its Application
title_full_unstemmed Beetle Colony Optimization Algorithm and its Application
title_sort Beetle Colony Optimization Algorithm and its Application
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Heng Zhang
Ziming Li
Xiangyuan Jiang
Xiaojing Ma
Jiyang Chen
Shuai Li
Yizhong Luan
Zhenyi Lv
Sile Ma
format Journal article
container_title IEEE Access
container_volume 8
container_start_page 128416
publishDate 2020
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2020.3008692
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning.
published_date 2020-07-23T04:04:50Z
_version_ 1756147000491900928
score 10.926129