An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

Zhan Wei Du; Yong Jian Yang; Yong Xiong Sun; Chi Jun Zhang; Tuan Liang Li

doi:10.4028/www.scientific.net/AMM.26-28.620

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 26-28An Improved Ant Colony Optimization Algorithm for...

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

Abstract:

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

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Periodical:

Applied Mechanics and Materials (Volumes 26-28)

Pages:

620-624

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.26-28.620

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Online since:

June 2010

Authors:

Zhan Wei Du, Yong Jian Yang, Yong Xiong Sun, Chi Jun Zhang, Tuan Liang Li

Keywords:

Ant Colony Algorithm (ACA), Magnetic Force, Traveling Salesman Problem (TSP)

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