Future Generation Computer Systems
Ant Colony Optimization
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We propose an ACO (Ant Colony Optimization) variation in which the evaporation rate, instead of being constant as is common in standard ACO algorithms, is a Gaussian random variable with non-negligible variance. In experimental results in the context of MAX-MIN Ant System (MMAS) and the Traveling Salesman Problem (TSP), we find that our variation performs considerably better than MMAS when the number of iterations is small, and that its performance is slightly better than MMAS when the number of iterations is large.