Paper Info
Title
Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems
Abstract
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.
Year
DOI
Venue
2011
10.1109/KSE.2011.39
Knowledge and Systems Engineering
Keywords
Field
DocType
ant colony optimization,large-scale constraint satisfaction problem,search inefficiency,hard binary csp instance,improving search efficiency,constraint satisfaction problems,hill-climbing approach,phase transition region,min-conflict hill-climbing,experimental simulation,naive ant system,phase transition,operations research,hill climbing,constraint satisfaction problem
Ant colony optimization algorithms,Hill climbing,Mathematical optimization,Computer science,Constraint theory,Inefficiency,Constraint satisfaction problem,Artificial intelligence,Machine learning,Binary number,Metaheuristic
Conference
ISBN
Citations 
PageRank 
978-1-4577-1848-9
0
0.34
References 
Authors
6
4
Name
Order
Citations
PageRank
Daiki Hayakawa100.68
Kazunori Mizuno24210.55
Hitoshi Sasaki310.70
Seiichi Nishihara47114.35