Title :
Ant Colony Optimizer as an Adaptive Classifier
Author :
Tayade, Achal ; Ragha, Leena
Author_Institution :
Dept. of Comput. Eng., Ramrao Adik Inst. of Technol., Navi Mumbai, India
Abstract :
The widespread popularity of Optimization Algorithm in many fields such as Optimization, Pattern Recognition, Feature Extraction, Feature Selection etc. is mainly due to their ability to solve optimization problems in path planning. Out of many kinds of optimization algorithms, Ant Colony Optimization Algorithm is one of the most popular optimization algorithms. Many algorithms that dynamically construct solution to Ant Colony Optimization have increased in recent years. Several ant colony optimization algorithms present a promising performance on combinatorial optimization problem. Among them, Max Min Ant System performs comparatively better for Travelling Salesman Problem as compared to Ant System and Ant Colony System. A method is proposed for Classification using Ant Colony Optimizer as an Adaptive Classifier. The Classifier using ACO may give more efficient and effective method for classification in the adaptive environment.
Keywords :
ant colony optimisation; combinatorial mathematics; adaptive classifier; ant colony optimizer; ant colony system; combinatorial optimization problem; feature extraction; feature selection; max min ant system; optimization algorithm; path planning; pattern recognition; travelling salesman problem; Ant colony optimization; Cities and towns; Classification algorithms; Computers; Equations; Heuristic algorithms; Optimization; Adaptive Classifier; Ant Colony Optimization; Combinatorial Optimization Problem; Optimization Algorithm; Path Planning;
Conference_Titel :
Communication, Information & Computing Technology (ICCICT), 2012 International Conference on
Conference_Location :
Mumbai
Print_ISBN :
978-1-4577-2077-2
DOI :
10.1109/ICCICT.2012.6398106