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Algorithms 2014, 7(2), 206-228; doi:10.3390/a7020206

Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC

1
Queen Mary University London, Mile End Road, London E1 4NS, UK
2
Goldsmiths College, University of London, New Cross, London SE14 6NW, UK
*
Author to whom correspondence should be addressed.
Received: 7 February 2014 / Revised: 8 April 2014 / Accepted: 25 April 2014 / Published: 5 May 2014
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
View Full-Text   |   Download PDF [317 KB, 15 May 2014; original version 5 May 2014]   |  

Abstract

Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of parameter estimation is a key element of many computer vision processes. Here, the task of hyperplane estimation in many dimensions is investigated. Following RANSAC (random sample consensus), a widely used optimisation technique and a standard technique for many parameter estimation problems, increasingly sophisticated data-driven forms of SDS are developed. The performance of these SDS algorithms and RANSAC is analysed and compared for a hyperplane estimation task. SDS is shown to perform similarly to RANSAC, with potential for tuning to particular search problems for improved results. View Full-Text
Keywords: optimisation; search; swarm; intelligence; stochastic; diffusion; RANSAC; hyperplane; estimation optimisation; search; swarm; intelligence; stochastic; diffusion; RANSAC; hyperplane; estimation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Williams, H.; Bishop, M. Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC. Algorithms 2014, 7, 206-228.

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