Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems
Oct. 14, 2011 to Oct. 17, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/KSE.2011.39
To efficiently solve large-scale constraint satisfaction problems, CSPs, we propose an ant colony optimization based meta-heuristics combined with the hill-climbing approach. In our method, in order to improve search inefficiency which happens due to slow reconstruction of assignments of values to variables in the naive ant system, AS, min-conflict hill-climbing is applied to some assignments constructed ones by AS. This method is applied to large-scale and hard binary CSP instances in phase transition regions, whose experimental simulations demonstrate that our method is more efficient than AS.
Daiki Hayakawa, Kazunori Mizuno, Hitoshi Sasaki, Seiichi Nishihara, "Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems", KSE, 2011, Knowledge and Systems Engineering, International Conference on, Knowledge and Systems Engineering, International Conference on 2011, pp. 200-204, doi:10.1109/KSE.2011.39