The ACO (Ant Colony Optimization Algorithm) is a probabilistic technique in computer science and operations research to solve computational problems that can be reduced to finding good paths through graphs. Artificial Ants stand for multi-agent approaches that are inspired by real ants behaviour. The prevailing model used is always the pheromone-based contact of biological insects. Artificial Ants combinations and local search algorithms have become a tool of choice for various optimization tasks that require some kind of graph, e.g. vehicle routing and internet routing. The burgeoning development in this area has led to conferences devoted exclusively to Artificial Ants, as well as various commercial applications by specialist companies such as Ant Optima.
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Research Article: Journal of Applied & Computational Mathematics
Scientific Tracks Abstracts: Journal of Biometrics & Biostatistics
Scientific Tracks Abstracts: Journal of Biometrics & Biostatistics
Scientific Tracks Abstracts: Journal of Applied & Computational Mathematics
Scientific Tracks Abstracts: Journal of Applied & Computational Mathematics
Scientific Tracks Abstracts: Journal of Mass Communication & Journalism
Scientific Tracks Abstracts: Journal of Mass Communication & Journalism
Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report
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