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Parallel Implementations of the Ant Colony Optimization Metaheuristic

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Intelligent Information and Database Systems (ACIIDS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9621))

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Abstract

The paper discusses different approaches to parallel implementation of the Ant Colony Optimization metaheuristic. The metaheuristic is applied to the well-known Travelling Salesman Problem. Although the Ant Colony Optimization approach is capable of delivering good quality solutions for the TSP it suffers from two factors: complexity and non-determinism. Overpopulating ants makes the ACO performance more predictable but increasing the number of ants makes the need for parallel processing even more apparent. The proposed Ant Colony Community (ACC) uses a coarse grained approach to parallelization. Two implementations using RMI and Sockets respectively are compared. Results of an experiment prove the ACC is capable of a substantial reduction of processing time.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Management, Technical University of Wrocław, Wrocław, Poland

    Andrzej Siemiński

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  1. Andrzej Siemiński
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Correspondence to Andrzej Siemiński .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Siemiński, A. (2016). Parallel Implementations of the Ant Colony Optimization Metaheuristic. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_60

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  • DOI: https://doi.org/10.1007/978-3-662-49381-6_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49380-9

  • Online ISBN: 978-3-662-49381-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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