Bees and Pollens with Communication Strategy for Optimization

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

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Due to interference phenomena among constrained dimensions of the multimodal optimization or complex constrained optimization problems, a local optimum is easily converged, rather than for the expected global optimum. The enhanced diversity agent in optimal algorithms is one of the solutions to this issue. This paper proposes a novel optimization algorithm, namely BPO, based on the communication of the bees in artificial bee colony optimization (ABC), with the pollen in flower pollination algorithm (FPA) to solve the multimodal optimization problems. A new communication strategy for Bees and Pollens is presented to explore and exploit the diversity of the algorithm. Six multimodal benchmark functions are used to verify the convergent behavior, the accuracy, and the speed of the proposed algorithm. Experimental results show that the proposed scheme increases the accuracy more than the original algorithms.

    Original languageEnglish
    Title of host publicationIntelligent Information and Database Systems
    EditorsTzung-Pei Hong, Ngoc Thanh Nguyen, Bogdan Trawinski, Hamido Fujita
    PublisherSpringer Berlin Heidelberg
    Pages651-660
    Number of pages10
    Volume9622
    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)
    Volume9622
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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  • Cite this

    Pan, T-S., Dao, T-K., Nguyen, T-T., Chu, S-C., & Pan, J-S. (2016). Bees and Pollens with Communication Strategy for Optimization. In T-P. Hong, N. T. Nguyen, B. Trawinski, & H. Fujita (Eds.), Intelligent Information and Database Systems (Vol. 9622, pp. 651-660). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9622). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_63