TY - GEN
T1 - Improved robustness through population variance in ant colony optimization
AU - Matthews, David C.
AU - Sutton, Andrew M.
AU - Hains, Doug
AU - Whitley, L. Darrell
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70350517405&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350517405&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03751-1_16
DO - 10.1007/978-3-642-03751-1_16
M3 - Conference contribution
AN - SCOPUS:70350517405
SN - 364203750X
SN - 9783642037504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 149
BT - Engineering Stochastic Local Search Algorithms - Designing, Implementing and Analyzing Effective Heuristics - Second International Workshop, SLS 2009, Proceedings
T2 - 2nd International Workshop on Engineering Stochastic Local Search Algorithms - Designing, Implementing and Analyzing Effective Heuristics, SLS 2009
Y2 - 3 September 2009 through 4 September 2009
ER -