Name
Abstract | ||
|---|---|---|
The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2010.09.149 | Expert Syst. Appl. |
Keywords | Field | DocType |
clustering problems,capacitated centered clustering problem,demand capacity,clustering search algorithm,genetic algorithm,metaheuristics,computational result,hybrid method,demand value,local search heuristics,n point,hybrid evolutionary algorithm,search space,minimum dissimilarity,search algorithm,local search | Canopy clustering algorithm,Mathematical optimization,CURE data clustering algorithm,Guided Local Search,Correlation clustering,Computer science,Beam search,Constrained clustering,Artificial intelligence,Cluster analysis,Machine learning,Best-first search | Journal |
Volume | Issue | ISSN |
38 | 5 | Expert Systems With Applications |
Citations | PageRank | References |
10 | 0.53 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Augusto Chaves | 1 | 116 | 10.24 |
| Luiz Antonio Nogueira Lorena | 2 | 498 | 36.72 |