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.
Highlights
In recent years there has been growing interest in swarm intelligence, a distributed mode of computation utilising interaction between simple agents [1]
Unlike stigmergetic communication employed in ant algorithms, which is based on the modification of the physical properties of a simulated environment, stochastic diffusion search (SDS) uses a form of direct communication between the agents similar to the tandem calling mechanism employed by one species of ant, Leptothorax acervorum, [7]
Remembering that as the dimensions increase, there is a combinatorial explosion of the number of possible hypotheses available, it stands to reason that a larger number of model hypotheses entertained by Coupled SDS (CSDS) and DDSDS should lead to better solutions being found
Summary
In recent years there has been growing interest in swarm intelligence, a distributed mode of computation utilising interaction between simple agents [1]. The problem solving ability of swarm intelligence methods emerges from positive feedback reinforcing potentially good solutions and the spatial/temporal characteristics of their agent interactions. Of these algorithms, stochastic diffusion search (SDS) was first described in 1989 as a population-based, pattern-matching algorithm [6]. Unlike stigmergetic communication employed in ant algorithms, which is based on the modification of the physical properties of a simulated environment, SDS uses a form of direct communication between the agents similar to the tandem calling mechanism employed by one species of ant, Leptothorax acervorum, [7]
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