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Local Termination Criteria for Stochastic Diffusion Search: A Comparison with the Behaviour of Ant Nest-Site Selection

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Computational Collective Intelligence (ICCCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9875))

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Abstract

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. Conversely, as a result of its resource allocation mechanism, the solutions found by Stochastic Diffusion Search enjoy excellent stability. Previous implementations of SDS have deployed complex stopping criteria derived from global properties of the agent population; this paper examines two new local SDS halting criteria and compares their performance with ‘quorum sensing’ - a natural termination criterion deployed in nature by some species of tandem-running ants. We empirically demonstrate that local termination criteria are almost as robust as the classical SDS termination criteria, whilst the average time taken to reach a decision is around three times faster.

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Notes

  1. 1.

    Standard SDS has previously been shown to be a global search algorithm [26] - it will eventually converge to the global best solution in a given search space; by removing agents form the swarm, relative to standard SDS the number of potential agents remaining available for explore-exploit behaviour is reduced; precisely how this reduction impacts the robustness of the algorithm [with respect to erroneous convergence to sub-optimal solutions] has yet to be fully established.

  2. 2.

    To facilitate the use of homogenous performance metrics, we assume that in a population of k agents, k single asynchronous updates corresponds to one standard synchronous iteration cycle.

  3. 3.

    Temnothorax ants are indeed sometimes recruited back to nests they have already visited, so there is potential for this ‘reinforcement recruitment’ process to play a role for ant colonies. For example, ‘reinforcement recruitment’ could cause ants to enter a post-quorum state at a lowered encounter rate. This would help extra rapid acceptance of a nest if there were only one new nest site available. This idea could be tested empirically, ideally in a complex arena that would promote tandem-running behaviour, allowing communication of preference.

  4. 4.

    \(\beta \) defines a “uniform random noise” hypothesis; an aggregate of all the possible hypotheses an agent could have other than the putative solution hypothesis.

  5. 5.

    These parameters define a problem analogous to the search space being infinitely large, wherein the only way an agent can adopt the ‘best’ solution is to receive it via diffusion from an active agent.

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Authors and Affiliations

  1. TCIDA, Goldsmiths, University of London, New Cross, London, UK

    J. Mark Bishop & Andrew O. Martin

  2. York Centre for Complex Systems Analysis and Department of Biology, University of York, York, UK

    Elva J. H. Robinson

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  1. J. Mark Bishop
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  2. Andrew O. Martin
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Correspondence to J. Mark Bishop .

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Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Management, Democritus University of Thrace, Orestiada, Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

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Bishop, J.M., Martin, A.O., Robinson, E.J.H. (2016). Local Termination Criteria for Stochastic Diffusion Search: A Comparison with the Behaviour of Ant Nest-Site Selection. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_44

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  • DOI: https://doi.org/10.1007/978-3-319-45243-2_44

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