Modification of the Elite Ant System in Order to Avoid Local Optimum Points in the Traveling Salesman Problem

TL;DR

This paper introduces a modified elite ant system algorithm for the traveling salesman problem that effectively escapes local optima by using global pheromone updates, improving solution quality over classical methods.

Contribution

The paper proposes a novel modification to the elite ant system that enhances its ability to avoid local optima through a new pheromone updating strategy.

Findings

Improved solution quality on standard TSP instances.

Outperforms classical EAS and is competitive with other meta-heuristics.

Effective in escaping local optima in TSP.

Abstract

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.