Document Type
Conference Proceeding
Publication Date
6-2011
Abstract
Ant colony optimization (ACO) algorithms can generate quality solutions to combinatorial optimization problems. However, like many stochastic algorithms, the quality of solutions worsen as problem sizes grow. In an effort to increase performance, we added the variable step size off-policy hill-climbing algorithm called PDWoLF (Policy Dynamics Win or Learn Fast) to several ant colony algorithms: Ant System, Ant Colony System, Elitist-Ant System, Rank-based Ant System, and Max-Min Ant System. Easily integrated into each ACO algorithm, the PDWoLF component maintains a set of policies separate from the ant colony's pheromone. Similar to pheromone but with different update rules, the PDWoLF policies provide a second estimation of solution quality and guide the construction of solutions. Experiments on large traveling salesman problems (TSPs) show that incorporating PDWoLF with the aforementioned ACO algorithms that do not make use of local optimizations produces shorter tours than the ACO algorithms alone.
Source Publication
IEEE Congress on Evolutionary Computation 2011
Recommended Citation
G. L. Peterson, C. B. Mayer and K. Couśin, "WoLF Ant," 2011 IEEE Congress of Evolutionary Computation (CEC), New Orleans, LA, USA, 2011, pp. 995-1002, https://doi.org/10.1109/CEC.2011.5949726
Comments
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AFIT Scholar furnishes the accepted version of this conference paper. The published version of record is available from IEEE via subscription at the DOI link in the citation below.
Funding: This paper was supported by the Air Force Office of Scientific Research, project number 2311/FX