skip to main content
10.1145/3419635.3419693acmotherconferencesArticle/Chapter ViewAbstractPublication PagescipaeConference Proceedingsconference-collections
research-article

Double Traction Strategy Particle Swarm Optimization Algorithm

Published: 18 September 2020 Publication History

Abstract

To solve the problem that the particle swarm optimization algorithm is easy to fall into the local optimum, a Double Traction Strategy Particle Swarm Optimization algorithm(DTSPSO) is proposed. Firstly, the algorithm divides the population into multiple subgroups by random grouping method, and then randomly assigns an optimization strategy to each subgroup, namely combined traction strategy or optimal traction strategy. In the iterative process, the algorithm will replace the optimization strategy or regroup the packets by monitoring the optimal update of the population optimal solution. The performance of DTSPSO algorithm and other five classical algorithms on six international benchmark functions is compared. The experimental results show that the DTSPSO algorithm exhibits relatively good comprehensive search performance in the optimization of unimodal and multimodal functions.

References

[1]
Kennedy J, Eberhart RC, Particle swarm optimization[J], Proc. IEEE Intl. Conf. on Neural Networks, 1995(4): 1942--1948.
[2]
ZHANG QK. Research on the particle swarm optimization and differential evolution algorithms[D]. Shandong University, 2017.
[3]
CAO JIANWEI, CHEN QINGKUI, ZHUANG SONGLIN. UPSO (Uniform Particle Swarm Optimization):An algorithm of dynamic overlay optimization for WSN[J]. Computer Science, 2014, 41(S1):255--257+269.
[4]
SHI YH, EBERHART R. A modified particle swarm optimizer[J]. Proceedings of the IEEE Conference on Evolutionary Computation, Anchorage, 4-9 May 1998: 69--73.
[5]
KNNEDY J, MENDES R. Population structure and particle swarm performance[P]. Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on, 2002.
[6]
ZHANG LIANG, YAO SHIJUN, CHEN CHUXIANG. Community detection algorithm based on local particle swarm optimization[J]. COMPUTER ENGINEERING AND DESIGN, 2016, 37(06):1500--1504.
[7]
PERAM T, VEERAMACHANENI K, MOHAN CK. Fitness-distance-ratio based particle swarm optimization[J]. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003: 174--181.

Cited By

View all
  • (2022)Comparative Analysis of a New Class of Symmetric and Asymmetric Supercapacitors Constructed on the Basis of ITO CollectorsEnergies10.3390/en1601030616:1(306)Online publication date: 27-Dec-2022

Index Terms

  1. Double Traction Strategy Particle Swarm Optimization Algorithm

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    CIPAE 2020: Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education
    October 2020
    527 pages
    ISBN:9781450387729
    DOI:10.1145/3419635
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 September 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Combined traction strategy
    2. Optimal traction strategy
    3. Particle swarm optimization algorithm
    4. Random grouping

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    CIPAE 2020

    Acceptance Rates

    CIPAE 2020 Paper Acceptance Rate 101 of 216 submissions, 47%;
    Overall Acceptance Rate 101 of 216 submissions, 47%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 14 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Comparative Analysis of a New Class of Symmetric and Asymmetric Supercapacitors Constructed on the Basis of ITO CollectorsEnergies10.3390/en1601030616:1(306)Online publication date: 27-Dec-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media