Robustness of Ant Colony Optimization to Noise

@article{Friedrich2015RobustnessOA,
  title={Robustness of Ant Colony Optimization to Noise},
  author={Tobias Friedrich and Timo K{\"o}tzing and Martin S. Krejca and Andrew M. Sutton},
  journal={Evolutionary Computation},
  year={2015},
  volume={24},
  pages={237-254}
}
Recently Ant Colony Optimization (ACO) algorithms have been proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses focus on combinatorial problems, such as path finding. We analyze an ACO algorithm in a setting where we try to optimize the simple OneMax test function, but with additive posterior noise sampled from a Gaussian distribution. Without noise the classical (μ+1)-EA outperforms any ACO algorithm, with smaller… CONTINUE READING
10 Citations
0 References
Similar Papers

Citations

Publications citing this paper.

Similar Papers

Loading similar papers…