Title

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

RIS ID

127709

Publication Details

Dong, G., Li, W., Shen, J., Wang, Y., Fu, X. & Guo, W. (2018). Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison. International Journal of Machine Learning and Computing, 8 (2), 98-103.

Abstract

In recent years some comparative studies have explored the use of parallel ant colony optimization (ACO) algorithms over the traditionally sequential ACOs to solve the traveling salesman problem (TSP). However, these studies did not take a systematical approach to assess the performance of both algorithms on a comparable ground. In this paper, we aim to make a comparison of both the quality of the solutions and the running time as a result of the application of a sequential ACO and a parallel ACO to Eil51, Eil76 and KroA100 on a normalized and thus, comparable ground.

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Link to publisher version (DOI)

http://dx.doi.org/10.18178/ijmlc.2018.8.2.670