Statistical Approach for Selecting Elite Ants
This work addresses the challenge of enhancing solution quality in ACO algorithms for researchers and practitioners in optimization, but it appears incremental as it builds on existing methods with new selection mechanisms.
The paper tackles the problem of improving Ant Colony Optimization (ACO) algorithms for combinatorial optimization by introducing new mechanisms to dynamically select elite ants using statistical tools, and it investigates their performance.
Applications of ACO algorithms to obtain better solutions for combinatorial optimization problems have become very popular in recent years. In ACO algorithms, group of agents repeatedly perform well defined actions and collaborate with other ants in order to accomplish the defined task. In this paper, we introduce new mechanisms for selecting the Elite ants dynamically based on simple statistical tools. We also investigate the performance of newly proposed mechanisms.