A novel discrete bat algorithm for solving the travelling salesman problem

• Published in: Neural computing & applications Vol. 27; no. 7; pp. 1853 - 1866
• Main Authors: Saji, Yassine, Riffi, Mohammed Essaid
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2016; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bats; Combinatorial analysis; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Heuristic methods; Image Processing and Computer Vision; Libraries; Optimization; Original Article; Probability and Statistics in Computer Science; Traveling salesman problem; Travelling salesman problem; NP-hard combinatorial optimization problem; Nature-inspired metaheuristic; Discrete bat-inspired algorithm
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-015-1978-9

The travelling salesman problem (TSP) is one of the well-known NP-hard combinatorial optimization and extensively studied problems in discrete optimization. The bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. In this paper we extend a discrete bat-inspired algorithm to solve the famous TSP. Although many algorithms have been used to solve TSP, the main objective of this research is to investigate this discrete version to achieve significant improvements, not only compared to traditional algorithms but also to another metaheuristics. Moreover, this study is based on a benchmark dataset of symmetric TSP from TSPLIB library.