Modification of the Elite Ant System in Order to Avoid Local Optimum Points in the Traveling Salesman Problem
This is an incremental improvement for solving combinatorial optimization problems like TSP, potentially benefiting researchers and practitioners in optimization.
The authors tackled the problem of local optima in the Traveling Salesman Problem by modifying the elite ant system algorithm to include a global updating criterion for escaping these points, resulting in improved efficiency across all tested instances and competitive performance with other algorithms.
This article presents a new algorithm which is a modified version of the elite ant system (EAS) algorithm. The new version utilizes an effective criterion for escaping from the local optimum points. In contrast to the classical EAC algorithms, the proposed algorithm uses only a global updating, which will increase pheromone on the edges of the best (i.e. the shortest) route and will at the same time decrease the amount of pheromone on the edges of the worst (i.e. the longest) route. In order to assess the efficiency of the new algorithm, some standard traveling salesman problems (TSPs) were studied and their results were compared with classical EAC and other well-known meta-heuristic algorithms. The results indicate that the proposed algorithm has been able to improve the efficiency of the algorithms in all instances and it is competitive with other algorithms.