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Local Termination Criteria for Swarm Intelligence: A Comparison Between Local Stochastic Diffusion Search and Ant Nest-Site Selection

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Transactions on Computational Collective Intelligence XXXII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 11370))

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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 chapter 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.

This paper offers extended discussion of results first presented at ICCCI 2016 (Halkidiki) and published in the conference proceedings [11].

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Notes

  1. 1.

    The simplifying assumption is that, by considering only the mean transition of agents between different clusters of agents, rather than the full probability distribution (as investigated in [51]), a sufficiently accurate model of SDS may be obtained.

  2. 2.

    It should be emphasised that this analogy is provided simply to illustrate the communication and feedback mechanisms at the heart of a stochastic diffusion search, and not as a heuristic to be employed by a group of hungry conference delegates.

  3. 3.

    The material in Sect. 6 was included at the suggestion of one of the reviewers of this manuscript; it forms a much extended and adapted version of the ‘practical’ analysis of SDS behaviour first presented by Myatt et al. in [48].

  4. 4.

    Thus multiple testing is very similar to the repeated metrics strategy deployed by real scouts in nest selection, albeit in the latter case scouts assess potential nest sites across a number of metrics using a weighted additive strategy [27].

  5. 5.

    Standard SDS has previously been shown to be a global search algorithm [51] - 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.

  6. 6.

    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.

  7. 7.

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

  8. 8.

    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

    Andrew O. Martin & J. Mark Bishop

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

    Elva J. H. Robinson

  3. Acrefield House, Belle Vue Estate, London, UK

    Darren R. Myatt

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  1. Andrew O. Martin
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  4. Darren R. Myatt
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  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Ryszard Kowalczyk

  3. Wrocław University of Economics, Wrocław, Poland

    Marcin Hernes

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Martin, A.O., Bishop, J.M., Robinson, E.J.H., Myatt, D.R. (2019). Local Termination Criteria for Swarm Intelligence: A Comparison Between Local Stochastic Diffusion Search and Ant Nest-Site Selection. In: Nguyen, N., Kowalczyk, R., Hernes, M. (eds) Transactions on Computational Collective Intelligence XXXII. Lecture Notes in Computer Science(), vol 11370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58611-2_3

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