Title :
Notice of Violation of IEEE Publication Principles
Swarm intelligence stability based on stochastic diffusion search
Author :
Abbas, Nizar H. ; Rao, Rameshwar
Author_Institution :
Dept. of Electron.&Commun. Eng., Osmania Univ., Hyderabad, India
Abstract :
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.
Keywords :
Markov processes; numerical stability; particle swarm optimisation; resource allocation; search problems; statistical distributions; Markov chains; optimal solution; population based search mechanisms; resource allocation; steady state probability distribution; stochastic diffusion search; swarm intelligence methods; swarm intelligence stability; Convergence; Distributed computing; Feedback; Genetic algorithms; Mechanical factors; Optimization methods; Particle swarm optimization; Resource management; Stability; Stochastic processes;
Conference_Titel :
Methods and Models in Computer Science, 2009. ICM2CS 2009. Proceeding of International Conference on
Conference_Location :
Delhi
Print_ISBN :
978-1-4244-5051-0
DOI :
10.1109/ICM2CS.2009.5397950