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Optimization of Generalized Networks Using Ant Colony Algorithm

This research presents a novel approach to optimizing generalized networks using an Ant Colony Optimization (ACO) algorithm. By modeling real processes, the study proposes a GN (Generalized Network) framework that integrates the characteristics of ACO, enabling efficient problem-solving for complex systems. The findings were shared at the 8th International Conference on Large-Scale Scientific Computations in Sozopol, highlighting significant advancements in network optimization. Acknowledgments are made to the Bulgarian National Science Fund and the Royal Society, UK, for their support in the research.

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Optimization of Generalized Networks Using Ant Colony Algorithm

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  1. A Generalized Netwith an ACO-algorithmOptimization Component Vassia Atanassova Institute of Information and Communication Technologies Stefka Fidanova Institute of Information and Communication Technologies Panagiotis Chountas Westminster University Krassimir Atanassov Institute of Biophysics and Biomedical Engineering 8th International Conference on Large-Scale Scientific Computations, Sozopol, 6-10 June 2011

  2. Generalized net of a real process

  3. GN transition

  4. Solving of an ACO problem

  5. GNprocess+ GNACO GN model of areal process GN model of the ACO optimization component

  6. Thank you for your attention! Acknowledgment to GrantsDID-02-29 “Modelling Processes with Fixed Development Rules” and DTK-02-44 “Effective Monte Carlo Methods for Large-Scale Scientific Problems” of Bulgarian NationalScience Fund and GrantJP100372 “Generalised net modelsand intuitionistic fuzzy sets in intelligent systems” of Royal Society, UK. Vassia AtanassovaInstitute of Information and Communication Technologies, BAS Stefka FidanovaInstitute of Information and Communication Technologies, BAS Panagiotis ChountasSchWestminster University Krassimir AtanassovInstitute of Biophysics and Biomedical Engineering, BAS

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