A Literature Survey on the Ant Quest Algorithm
G. Guru, Kesava Dasu, P. Bala Krishna Prasad · 2014
Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. The web have paved way to information sources belonging to same domain to be distributed that are structurally (to some extent) and semantically heterogeneous with the invert of internet. In order to achieve semantic interoperability within these information sources, this exists at various levels such as at data, operating system or due to hardware heterogeneity. Previously we use a non-instance learning-based approach that transforms the ontology mapping problem to a binary classification problem and utilizes machine learning techniques as a solution as same as other machine learning based approaches. To evaluate the binary classification problem, two experiments (i.e., within-task vs. cross-task) are implemented and the SVM algorithm is applied. As our survey describes the flexible optimization, Ant Colony Optimization, by the Ant Quest Algorithm. As we use the bee association for the finding the direction simulation for the Ant Quest Algorithm. Scientific literature is prolific both on exact and on heuristic solution methods developed to solve optimization problems. Heuristic methods, do not guarantee to determine a global optimal solution for a problem but are usually able to find a good solution rapidly, perhaps a local optimum, and require less computational resources. Heuristic methods, do not guarantee to determine a global optimal solution for a problem but are usually able to find a good solution rapidly, perhaps a local optimum, and require less computational resources. Ant Quest Algorithm Depend on Ant Province Optimization using Ant association. Ant Province optimization direction-finding algorithm abide in the successive acquire of direction-finding information during path inspect and detection by little organizing containers, the ants. As our research survey says that the Ant colony optimization shows the behavior of the optimization and the finding of the artificial ant and minimal cost path.