By the same authors

Local termination criteria for Swarm Intelligence: a comparison between local Stochastic Diffusion Search and ant nest-site selection

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Publication details

Title of host publicationTransactions on Computational Collective Intelligence
DateAccepted/In press - 15 May 2018
DatePublished (current) - 18 Dec 2018
Number of pages27
Place of PublicationBerlin, Heidelberg
Original languageEnglish

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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.

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    Research areas

  • decision-making, search algorithms, stochastic search, Quorum Sensing, quorum threshold

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