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Notice of Violation of IEEE Publication Principles
Swarm intelligence stability based on stochastic diffusion search

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2 Author(s)
Nizar H. Abbas ; Department of Electronics & Communication Engineering, Osmania University, Hyderabad, India ; Rameshwar Rao

Notice of Violation of IEEE Publication Principles

"Swarm Intelligence Stability Based on Stochastic Diffusion Search"
by Nazir H. Abbas and Rameshwar Rao
in the 2009 Proceedings of International Conference on Methods and Models in Computer Science (ICM2CS 2009), December 2009

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Swarm intelligence search: topology and stability"
by Slawomir J Nasuto, Mark Bishop, Kris De Meyer
Submitted to Natural Computing, 2008

This paper explore the swarm intelligence stability based on stochastic diffusion search (SDS) which is capable to find rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of the positive feedback in its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability. The standard SDS relies on the all-to-all connectivity of agents. We relax the assumption about such interaction patterns and investigate various connection topologies. We also characterize the stability problems in terms of their steady state probability distribution, because SDS behaves in the limit as an ensemble of identical ergodic Markov chains.

Published in:

Methods and Models in Computer Science, 2009. ICM2CS 2009. Proceeding of International Conference on

Date of Conference:

14-15 Dec. 2009