Unsupervised Updation Strategies for ACO Algorithms
Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the optimized solution. In this paper, the authors propose new updating mechanism based on clustering techniques, an unsupervised learning mechanism aimed at exploring the nearby solutions region. They also report in detail the impact on performance due to integration of cluster and ACO.