skip to main content
10.1145/3379310.3379328acmotherconferencesArticle/Chapter ViewAbstractPublication PagesapitConference Proceedingsconference-collections
research-article

A Fast Particle Swarm Optimization Algorithm by Refining the Global Best Solution

Published: 29 March 2020 Publication History

Abstract

A Fast Particle Swarm Optimization (FPSO) is proposed to improve the convergence response speed for some potential application scenarios such as the online or dynamical optimization environment which requires the fast convergence ability of an optimizer. Classical gradient-based optimization methods are good at finding the local optimal value of a convex region yet usually failure in searching the global optimal value of a multimodal problem. To further develop the characteristics of PSO with respect to the fast convergence and the global optimization, a pseudo-gradient method is proposed for calculating the approximate gradient at the location of the global best solution (gBest) of a swarm to refine the convergence accuracy of the gBest so as to accelerate the local convergence speed. The experimental results show that the performance of the proposed algorithm is significantly better than those of the five chosen competitive algorithms on a series of benchmark test functions with different characteristics. Furthermore, the sensitivity of the new introduced parameter in the proposed algorithm is empirically analyzed by a special experiment for recommending its best range of value.

References

[1]
Woo, H. W., H. H. Kwon, and M.-J. Tahk. 2004. A Hybrid Method of Evolutionary Algorithms and Gradient Search. In 2nd International Conference on Autonomous Robots and Agents, (Palmerston North, New Zealand, December 13-15, 2004). Citeseer, Pennsylvania, P.
[2]
Eberhart, R., and J. Kennedy. 1995. Particle Swarm Optimization. In Proceedings of the IEEE international conference on neural networks, (Perth, WA, Australia, November 27 - December 01, 1995). ICNN '95. IEEE, Piscataway, P, 1942-1948. DOI= https://dx.doi.org/10.1109/ICNN.1995.488968.
[3]
Bonyadi, M. R., and Z. Michalewicz. 2017. Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review. Evol Comput. 25, 1 (Mar. 2017), 1--54. DOI= https://dx.doi.org/10.1162/EVCO_r_00180.
[4]
Shi, Y., and R. Eberhart. 1998. A Modified Particle Swarm Optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, (Anchorage, AK, USA, May 04-09, 1998). WCCI '98. IEEE, Piscataway, P, 69--73. DOI= https://dx.doi.org/10.1109/icec.1998.699146.
[5]
Poli, R. 2008. Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications. 2008 (Feb. 2008), 1--10. DOI= https://dx.doi.org/10.1155/2008/685175.
[6]
Luo, J., and Y. Gao. 2019. Cooperative Particle Swarm Optimization Algorithm with Cloud Mutation Operator Based on Normal Cloud Model. International Journal of Machine Learning and Computing. 9, 5 (Oct. 2019), 554--560. DOI= https://dx.doi.org/10.18178/ijmlc.2019.9.5.839
[7]
Bonyadi, M., X. Li, and Z. Michalewicz. 2013. A Hybrid Particle Swarm with Velocity Mutation for Constraint Optimization Problems. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - (Amsterdam, The Netherlands, July 06-10, 2013). GECCO '13. ACM, New York, NK, 1--8. DOI= https://dx.doi.org/10.1145/2463372.2463378.
[8]
Liang, J. J., A. K. Qin, P. N. Suganthan, and S. Baskar. 2006. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transactions on Evolutionary Computation. 10, 3 (Jun. 2006), 281--295. DOI= https://dx.doi.org/10.1109/tevc.2005.857610.
[9]
Wang, F., H. Zhang, K. Li, Z. Lin, J. Yang, and X.-L. Shen. 2018. A Hybrid Particle Swarm Optimization Algorithm Using Adaptive Learning Strategy. Information Sciences. 436 (Apr. 2018), 162--177. DOI= https://dx.doi.org/10.1016/j.ins.2018.01.027.
[10]
Dieu, V. N., P. Schegner, and W. Ongsakul. 2013. Pseudo-Gradient Based Particle Swarm Optimization Method for Nonconvex Economic Dispatch. Power, Control and Optimization. Lecture Notes in Electrical Engineering. Springer, Heidelberg, 2013. DOI= https://dx.doi.org/10.1007/978-3-319-00206-4_1.
[11]
Wang, Y., B. Li, T. Weise, J. Wang, B. Yuan, and Q. Tian. 2011. Self-Adaptive Learning Based Particle Swarm Optimization. Information Sciences. 181, 20 (Nov. 2011), 4515--4538. DOI= https://dx.doi.org/10.1016/j.ins.2010.07.013.
[12]
Li, C., S. Yang, and T. T. Nguyen. 2012. A Self-Learning Particle Swarm Optimizer for Global Optimization Problems. IEEE Trans Syst Man Cybern B Cybern. 42, 3 (Jun. 2012), 627--646. DOI= https://dx.doi.org/10.1109/TSMCB.2011.2171946.
[13]
Ratnaweera, A., S. K. Halgamuge, and H. C. Watson. 2004. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation. 8, 3 (Jun. 2004), 240--255. DOI= https://dx.doi.org/10.1109/tevc.2004.826071.
[14]
Salomon, R. 1998. Evolutionary Algorithms and Gradient Search: Similarities and Differences. IEEE Transactions on Evolutionary Computation. 2, 2 (Jul. 1998), 45--55. DOI= https://dx.doi.org/10.1109/4235.728207.
[15]
Wen, J., Q. Wu, L. Jiang, and S. Cheng. 2003. Pseudo-Gradient Based Evolutionary Programming. Electronics Letters. 39, 7 (Apr. 2003), 631--632. DOI= https://dx.doi.org/10.1049/el:20030404.
[16]
Cavalca, D. L., and R. A. Fernandes. 2018. Gradient-Based Mechanism for Pso Algorithm: A Comparative Study on Numerical Benchmarks. In 2018 IEEE Congress on Evolutionary Computation (CEC), (Rio de Janeiro, Brazil, July 08-13, 2018). CEC '18. IEEE, Piscataway, P, 1--7. DOI= https://dx.doi.org/10.1109/cec.2018.8477798

Index Terms

  1. A Fast Particle Swarm Optimization Algorithm by Refining the Global Best Solution

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    APIT '20: Proceedings of the 2020 2nd Asia Pacific Information Technology Conference
    January 2020
    185 pages
    ISBN:9781450376853
    DOI:10.1145/3379310
    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: 29 March 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. computational intelligence (CI)
    2. evolutionary computation
    3. particle swarm optimization (PSO)
    4. pseudo-gradient

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • National Natural Science Foundation of China
    • Seism Science & Technology Spark Program of China Earthquake Administrator

    Conference

    APIT 2020

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 109
      Total Downloads
    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 30 Jan 2025

    Other Metrics

    Citations

    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