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Transparent and Efficient Parallelization of Swarm Algorithms

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Published:06 June 2016Publication History
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Abstract

This article presents an approach for the efficient and transparent parallelization of a large class of swarm algorithms, specifically those where the multiagent paradigm is used to implement the functionalities of bioinspired entities, such as ants and birds. Parallelization is achieved by partitioning the space on which agents operate onto multiple regions and assigning each region to a different computing node. Data consistency and conflict issues, which can arise when several agents concurrently access shared data, are handled using a purposely developed notion of logical time. This approach enables a transparent porting onto parallel/distributed architectures, as the developer is only in charge of defining the behavior of the agents, without having to cope with issues related to parallel programming and performance optimization. The approach has been evaluated for a very popular swarm algorithm, the ant-based spatial clustering and sorting of items, and results show good performance and scalability.

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        • Published in

          cover image ACM Transactions on Autonomous and Adaptive Systems
          ACM Transactions on Autonomous and Adaptive Systems  Volume 11, Issue 2
          Special Section on Best Papers from SASO 2014 and Regular Articles
          July 2016
          267 pages
          ISSN:1556-4665
          EISSN:1556-4703
          DOI:10.1145/2952298
          Issue’s Table of Contents

          Copyright © 2016 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 June 2016
          • Revised: 1 February 2016
          • Accepted: 1 February 2016
          • Received: 1 February 2015
          Published in taas Volume 11, Issue 2

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