Accelerating the fireworks algorithm with an estimated convergence point

Jun Yu, Hideyuki Takagi, Ying Tan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    12 Citations (Scopus)

    Abstract

    We propose an acceleration method for the fireworks algorithms which uses a convergence point for the population estimated from moving vectors between parent individuals and their sparks. To improve the accuracy of the estimated convergence point, we propose a new type of firework, the synthetic firework, to obtain the correct of the local/global optimum in its local area’s fitness landscape. The synthetic firework is calculated by the weighting moving vectors between a firework and each of its sparks. Then, they are used to estimate a convergence point which may replace the worst firework individual in the next generation. We design a controlled experiment for evaluating the proposed strategy and apply it to 20 CEC2013 benchmark functions of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs each. The experimental results and the Wilcoxon signed-rank test confirm that the proposed method can significantly improve the performance of the canonical firework algorithm.

    Original languageEnglish
    Title of host publicationAdvances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
    EditorsYing Tan, Yuhui Shi, Qirong Tang
    PublisherSpringer Verlag
    Pages263-272
    Number of pages10
    ISBN (Print)9783319938141
    DOIs
    Publication statusPublished - 2018
    Event9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China
    Duration: Jun 17 2018Jun 22 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10941 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other9th International Conference on Swarm Intelligence, ICSI 2018
    Country/TerritoryChina
    CityShanghai
    Period6/17/186/22/18

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • General Computer Science

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