Martin, Andrew O., Bishop, J. M., Robinson, Elva Joan Hilda orcid.org/0000-0003-4914-9327 et al. (1 more author) (2018) Local termination criteria for Swarm Intelligence:a comparison between local Stochastic Diffusion Search and ant nest-site selection. In: Transactions on Computational Collective Intelligence. Lecture Notes in Computer Science . Springer , Berlin, Heidelberg , pp. 140-166.
Abstract
Stochastic diffusion search (SDS) is a global Swarm Intelligence optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Although population based decision mechanisms employed by many Swarm Intelligence methods can suffer poor convergence resulting in ill-defined halting criteria and loss of the best solution, as a result of its resource allocation mechanism, the solutions found by Stochastic Diffusion Search enjoy excellent stability. Previous implementations of SDS have deployed stopping criteria derived from global properties of the agent population; this paper examines new local SDS halting criteria and compares their performance with ‘quorum sensing’ (a termination criterion naturally deployed by some species of tandem-running ants). In this paper we discuss two experiments investigating the robustness and efficiency of the new local termination criteria; our results demonstrate these to be (a) effectively as robust as the classical SDS termination criteria and (b) almost three times faster.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Keywords: | decision-making,search algorithms,stochastic search,Quorum Sensing,quorum threshold |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Biology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 04 Dec 2018 17:00 |
Last Modified: | 16 Oct 2024 11:01 |
Published Version: | https://doi.org/10.1007/978-3-662-58611-2_3 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-662-58611-2_3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139491 |
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