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Convergence analysis of stochastic diffusion search

Nasuto, S. and Bishop, M. (1999) Convergence analysis of stochastic diffusion search. Journal of Parallel Algorithms and Applications, 14 (2). pp. 89-107. ISSN 1744-5779

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To link to this article DOI: 10.1080/10637199808947380

Abstract/Summary

In this paper we present a connectionist searching technique - the Stochastic Diffusion Search (SDS), capable of rapidly locating a specified pattern in a noisy search space. In operation SDS finds the position of the pre-specified pattern or if it does not exist - its best instantiation in the search space. This is achieved via parallel exploration of the whole search space by an ensemble of agents searching in a competitive cooperative manner. We prove mathematically the convergence of stochastic diffusion search. SDS converges to a statistical equilibrium when it locates the best instantiation of the object in the search space. Experiments presented in this paper indicate the high robustness of SDS and show good scalability with problem size. The convergence characteristic of SDS makes it a fully adaptive algorithm and suggests applications in dynamically changing environments.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18633
Uncontrolled Keywords:Probabilistic search, Best fit matching, Markov chain modeling, Distributed processing
Additional Information:Journal now published as: International Journal of Parallel, Emergent and Distributed Systems
Publisher:Taylor & Francis

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