TY  - JOUR
A1  - Friedrich, Tobias
A1  - Kötzing, Timo
A1  - Krejca, Martin Stefan
A1  - Sutton, Andrew M.
T1  - Robustness of Ant Colony Optimization to Noise
T2  - Evolutionary computation
N2  - Recently, ant colony optimization (ACO) algorithms have proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses have focused on combinatorial problems such as path finding. We rigorously analyze an ACO algorithm optimizing linear pseudo- Boolean functions under additive posterior noise. We study noise distributions whose tails decay exponentially fast, including the classical case of additive Gaussian noise. Without noise, the classical (mu + 1) EA outperforms any ACO algorithm, with smaller mu being better; however, in the case of large noise, the (mu + 1) EA fails, even for high values of mu (which are known to help against small noise). In this article, we show that ACO is able to deal with arbitrarily large noise in a graceful manner; that is, as long as the evaporation factor. is small enough, dependent on the variance s2 of the noise and the dimension n of the search space, optimization will be successful. We also briefly consider the case of prior noise and prove that ACO can also efficiently optimize linear functions under this noise model.
KW  - Ant colony optimization
KW  - Noisy Fitness
KW  - Theory
KW  - Run time analysis
Y1  - 2016
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/45268
SN  - 1063-6560
SN  - 1530-9304
VL  - 24
SP  - 237
EP  - 254
PB  - MIT Press
CY  - Cambridge
ER  -