A novel discrete bat algorithm for solving the travelling salesman problem. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Saji, Yassine; Riffi, Mohammed
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 1853
• Page End: 1866
• Publication Date: 2016-10
• Subjects: Travelling salesman problem -- NP-hard combinatorial optimization problem -- Nature-inspired metaheuristic -- Discrete bat-inspired algorithm; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1978-9
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract 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.