Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization

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

    Research output: Chapter in Book/Report/Conference proceedingChapter

    4 Citations (Scopus)

    Abstract

    Easy convergence to a local optimum, rather than global optimum could unexpectedly happen in practical multimodal optimization problems due to interference phenomena among physically constrained dimensions. In this paper, an altering strategy for dynamic diversity Flower pollination algorithm (FPA) is proposed for solving the multimodal optimization problems. In this proposed method, the population is divided into several small groups. Agents in these groups are exchanged frequently the evolved fitness information by using their own best historical information and the dynamic switching probability is to provide the diversity of searching process. A set of the benchmark functions is used to test the quality performance of the proposed method. The experimental result of the proposed method shows the better performance in comparison with others methods.

    Original languageEnglish
    Title of host publicationIntelligent Information and Database Systems
    EditorsNgoc Thanh Nguyen, Bogdan Trawinski, Tzung-Pei Hong, Hamido Fujita
    PublisherSpringer Berlin Heidelberg
    Pages440-448
    Number of pages9
    ISBN (Print)9783662493809
    DOIs
    Publication statusPublished - 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9621
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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  • Cite this

    Pan, J-S., Dao, T-K., Nguyen, T-T., Chu, S-C., & Pan, T-S. (2016). Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization. In N. T. Nguyen, B. Trawinski, T-P. Hong, & H. Fujita (Eds.), Intelligent Information and Database Systems (pp. 440-448). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9621). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_42