An ACO algorithm for the most probable explanation problem
HP Guo, PR Boddhireddy, WH Hsu · Lecture Notes in Artificial Intelligence · 2004
We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference After tuning its parameters settings, we compare ANT-MPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search Experimental results on both artificial and real networks show that in general ANT-MPE outperforms all other algorithms, but on networks with unskewed distributions local search algorithms are slightly better The result reveals the nature of ACO as a combination of both sampling and local search It helps us to understand ACO better, and, more important, it also suggests a possible way to improve ACO.