A Hybrid Hierarchical Heuristic-ACO With Local Search Applied to Travelling Salesman Problem, AS-FA-Ls

Nizar Rokbani, Pavel Kromer, Ikram Twir and Adel M. Alimi
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Nizar Rokbani: High Institute of Applied Science and Technology of Sousse, University of Sousse, Tunisia & REGIM-Lab, University of Sfax, Tunisia & National Engineering School of Sfax, Tunisia
Pavel Kromer: Department of Computer Science, FEECS, VSB - Technical University of Ostrava, Czech Republic
Ikram Twir: High Institute of Applied Science and Technology of Sousse, University of Sousse, Tunisia
Adel M. Alimi: REGIM-Lab, University of Sfax, Tunisia & National Engineering School of Sfax, Tunisia

International Journal of System Dynamics Applications (IJSDA), 2020, vol. 9, issue 3, 58-73

Abstract: The combinatorial optimization problem is attracting research because they have a wide variety of applications ranging from route planning and supply chain optimization to industrial scheduling and the IoT. Solving such problems using heuristics and bio-inspired techniques is an alternative to exact solutions offering acceptable solutions at fair computational costs. In this article, a new hierarchical hybrid method is proposed as a hybridization of Ant Colony Optimization (ACO), Firefly Algorithm (FA), and local search (AS-FA-Ls). The proposed methods are compared to similar techniques on the traveling salesman problem, (TSP). ACO is used in a hierarchical collaboration schema together with FA which is used to adapt ACO parameters. A local search strategy is used which is the 2 option method to avoid suboptimal solutions. A comparative review and experimental investigations are conducted using the TSP benchmarks. The results showed that AS-FA-Ls returned better results than the listed works in the following cases: berlin52, st70, eil76, rat99, kroA100, and kroA200. Computational investigations allowed determining a set of recommended parameters to be used with ACO for the TSP instances of the study.

Date: 2020
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