Hybrid Particle Swarm Optimization with Bat Algorithm

Tien Szu Pan, Thi Kien Dao, Trong The Nguyen, Shu Chuan Chu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    28 Citations (Scopus)

    Abstract

    In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

    Original languageEnglish
    Title of host publicationGenetic and Evolutionary Computing
    Subtitle of host publicationProceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China
    EditorsHui Sun, Ching-Yu Yang, Chun-Wei Lin, Jeng-Shyang Pan, Vaclav Snasel, Ajith Abraham
    Place of PublicationSwitzerland
    PublisherSpringer-Verlag
    Pages37-47
    Number of pages11
    ISBN (Electronic)9783319122861
    ISBN (Print)9783319122854
    DOIs
    Publication statusPublished - 2015
    Event8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014 - Nanchang, China
    Duration: 18 Oct 201420 Oct 2014
    Conference number: 8

    Publication series

    NameAdvances in Intelligent Systems and Computing
    Volume329
    ISSN (Print)2194-5357

    Conference

    Conference8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014
    CountryChina
    CityNanchang
    Period18/10/1420/10/14

    Keywords

    • Bat Algorithm Optimizations
    • Hybrid Particle Swarm Optimization with Bat Algorithm
    • Particle Swarm Optimization Algorithm
    • Swarm Intelligence

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