Publication:
Performance of Equilibrium Optimizer for the Traveling Salesman Problem
Performance of Equilibrium Optimizer for the Traveling Salesman Problem
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Date
2021
Authors
Quyen Nguyen
Minh-Phung Bui
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Research Projects
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Abstract
This paper presents a new method based on Equilibrium Optimizer (EO) algorithm that is inspired from the
mass balance of a control volume for traveling salesman problem (TSP). For enhancing the efficiency of EO, the 2-
opt movement algorithm is used to update the solution generated by EO. The efficiency of the proposed EO for the
TSP problem has been compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on different
instances consisting of the 14-city, 30-city, 48-city and 52-city. The calculated results show that for the large scale
instances such as 48-city and 52-city, EO has found the better tour than PSO. In comparison with GA, EO has ability
finding the best tour with the smaller mean and standard deviation. The comparisons with previous methods in
literature have also demonstrated that EO has ability to search the better tour than other methods. Thus, the proposed
EO can be a potential method for the TSP problem.
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Keywords
"Equilibrium optimizer,
Genetic algorithm,
Particle swarm optimization,
Traveling salesman problem,
2- opt."