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

  • Andrew O. Martin
  • J. Mark BishopEmail author
  • Elva J. H. Robinson
  • Darren R. Myatt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11370)

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.

Keywords

Collective decision making Ant nest selection Stochastic Diffusion Search Swarm Intelligence Global Search 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Andrew O. Martin
    • 1
  • J. Mark Bishop
    • 1
    Email author
  • Elva J. H. Robinson
    • 2
  • Darren R. Myatt
    • 3
  1. 1.TCIDA, Goldsmiths, University of LondonNew Cross, LondonUK
  2. 2.York Centre for Complex Systems Analysis and Department of BiologyUniversity of YorkYorkUK
  3. 3.Acrefield House, Belle Vue EstateLondonUK

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