Research Paper
Year: 2020 | Month: February | Volume: 7 | Issue: 2 | Pages: 150-154
Performance Improvement of Ant Colony Optimization Algorithm Using Multi-Attribute Rating Simple Technique Exploiting Ranks
Subhan Hafiz Nanda Ginting1, Sawaluddin2, Erna Budhiarti Nababan2
1Postgraduate Students at Universitas Sumatera Utara, Medan, Indonesia
2Postgraduate Lecturer at Universitas Sumatera Utara, Medan, Indonesia
Corresponding Author: Subhan Hafiz Nanda Ginting
ABSTRACT
The other research to improve the performance of the ant colony optimization (ACO) algorithm by using the simple multi attribute rating technique exploiting ranks (SMARTER) algorithm in getting the fastest time in providing alternative path solutions that can be used in solving TSP case examples and producing new algorithms that are better at solving instances of nearest path search cases. The data used for this study is in the form of a 200-city problem a dataset from the krolak felts and nelson repository. The ACO algorithm functions to optimize the total distance for the TSP dataset while the SMARTER algorithm is used to provide recommendations for the best routes based on the total trip distance generated by ACO. The experimental results obtained of the comparison criteria in this study are the optimum distance (best), average optimum distance (AVG) and processing time (running time). The three criteria, the ACO-SMARTER algorithm is superior except for the AVG criteria for large datasets pr439 and pr1002. Overall the ACO-SMARTER algorithm is better by 26.17% compared to the ACO algorithm in the research of Junjie, P. & Dingwei, W. (2016).
Keywords: Ant Colony Optimization (ACO), Simple Multi Attribute Rating Technique Exploiting Ranks (SMARTER)
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