Peer-to-peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms

Bánhelyi, Balázs and Biazzini, Marco and Montresor, Alberto and Jelasity, Márk (2009) Peer-to-peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms. Lecture Notes in Computer Science; 5484 . Heidelberg : Springer Verlag, pp. 87-92. ISBN 978-3-642-01128-3

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    Abstract. Recent developments in the area of peer-to-peer (P2P) computing have enabled a new generation of fully-distributed global optimization algorithms via providing self-organizing control and load balancing mechanisms in very large scale, unreliable networks. Such decentralized networks (lacking a GRID-style resource management and scheduling infrastructure) are an increasingly important platform to exploit. So far, little is known about the scaling and reliability of optimization algorithms in P2P environments. In this paper we present empirical results comparing two algorithms for real-valued search spaces in large-scale and unreliable networks. Some interesting, and perhaps counter-intuitive findings are presented: for example, failures in the network can in fact significantly improve performance under some conditions. The two algorithms that are compared are a known distributed particle swarm optimization (PSO) algorithm and a novel P2P branch-and-bound (B&B) algorithm based on interval arithmetic. Although our B&B algorithm is not a black-box heuristic, the PSO algorithm is competitive in certain cases, in particular, in larger networks. Comparing two rather different paradigms for solving the same problem gives a better characterization of the limits and possibilities of optimization in P2P networks.

    Item Type: Conference paper
    Publisher policy set phrase: The original publication is available at
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Repository staff approval on: 23 Jan 2009

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