Comparative analysis of solving traveling salesman problem using artificial intelligence algorithms

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management

Volume

2018-January

First Page

1

Last Page

6

Publication Date

7-2-2017

Abstract

This paper aims to provide a comparative study of the different artificial intelligence (AI) algorithms applied to solve the traveling salesman problem (TSP). Four (4) AI algorithms such as genetic algorithm, nearest neighbor, ant colony optimization, and neuro-fuzzy are executed in MatLab software to determine which among these techniques will provide the least execution time to solve a TSP. The objective of comparing and analyzing each AI algorithm - as applied to a single problem with the different program execution - is to identify if significant difference in execution time could lead to significant saving in energy consumption. The simulations using MatLab resulted to strong correlation at an R2 of 0.95 in the average execution time with the number of code lines, but do not give a significant execution time variance as when ANOVA and t-test measures were performed. The result of this paper could be used as a basis in the design phase of software development life cycle to arrive into an energy efficient software application with respect to time needed to execute a program. © 2017 IEEE.

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Digitial Object Identifier (DOI)

10.1109/HNICEM.2017.8269423

Disciplines

Electrical and Computer Engineering | Electrical and Electronics

Keywords

Artificial intelligence; Algorithms; Computer software—Development; Traveling salesman problem

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