Dynamic Search Initialisation Strategies for Multi-Objective Optimisation in Peer-to-peer Networks

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Author(s)
Scriven, Ian
Lewis, Andrew
Mostaghim, Sanaz
Griffith University Author(s)
Year published
2009
Metadata
Show full item recordAbstract
Peer-to-peer based distributed computing environments can be expected to be dynamic to greater of lesser degree. While node losses will not usually lead to catastrophic failure of a population-based optimisation algorithm, such as particle swarm optimisation, performance will be degraded unless the lost computational power is replaced. When resources are replaced, one must consider how to utilise newly available nodes as well as the loss of existing nodes. In order to take advantage of newly available nodes, new particles must be generated to populate them. This paper proposes two methods of generating new particles ...
View more >Peer-to-peer based distributed computing environments can be expected to be dynamic to greater of lesser degree. While node losses will not usually lead to catastrophic failure of a population-based optimisation algorithm, such as particle swarm optimisation, performance will be degraded unless the lost computational power is replaced. When resources are replaced, one must consider how to utilise newly available nodes as well as the loss of existing nodes. In order to take advantage of newly available nodes, new particles must be generated to populate them. This paper proposes two methods of generating new particles during algorithm execution and compares the performance of each approach, then investigates a hybridised approach incorporating both mechanisms.
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View more >Peer-to-peer based distributed computing environments can be expected to be dynamic to greater of lesser degree. While node losses will not usually lead to catastrophic failure of a population-based optimisation algorithm, such as particle swarm optimisation, performance will be degraded unless the lost computational power is replaced. When resources are replaced, one must consider how to utilise newly available nodes as well as the loss of existing nodes. In order to take advantage of newly available nodes, new particles must be generated to populate them. This paper proposes two methods of generating new particles during algorithm execution and compares the performance of each approach, then investigates a hybridised approach incorporating both mechanisms.
View less >
Conference Title
2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5
Copyright Statement
© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper was first published in the Proceedings of IEEE Congress on Evolutionary Computation, 2009.
Subject
Optimisation
Distributed computing and systems software not elsewhere classified