Future Generation Computer Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Improved Lower Limits for Pheromone Trails in Ant Colony Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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Ant Colony Optimization algorithms are population-based Stochastic Local Search algorithms that mimic the behavior of ants, simulating pheromone trails to search for solutions to combinatorial optimization problems. This paper introduces Population Variance, a novel approach to ACO algorithms that allows parameters to vary across the population over time, leading to solution construction differences that are not strictly stochastic. The increased exploration appears to help the search escape from local optima, significantly improving the robustness of the algorithm with respect to suboptimal parameter settings.